Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F17/10—Complex mathematical operations
  • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
  • G16B5/30—Dynamic-time models
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
  • G16B5/10—Boolean models
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
  • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
  • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

• This invention is a method or system to construct at least one profile representing how the actions of an object of investigation are coordinated, the prof ⁇ le(s) being based on computed measures of longitudinal association or temporal contingency that quantify patterns of interaction in repeated measures or time series data that include two or more variables for one individual.
• the statistical method is a primary computational method to inform the process of model building.
• This invention addresses certain fundamental problems that derive from limitations often encountered when the statistical method is a primary means to inform the process of creating, verifying and refining mathematical models. By addressing these problems, this invention facilitates many scientific investigations and practical arts that may benefit from scientific knowledge.
• the Appendix is an outline that helps reveal the logical structure of this application.
• Model builders generally identify an object to model and abstract variables that may be relevant to its functioning. Then modelers determine how the variables interact in order to inform the process of model construction. To a large extent, mathematical models are verified by the extent to which they accurately represent interactions of their objects in the real world.
• the statistical method is an important tool for constructing many mathematical models.
• the statistical method includes various measures and procedures for revealing interactions that can be modeled.
• a primary problem addressed by this invention derives from the fact that the statistical method is best suited to address objects of investigation that are collective entities. Groups, samples, and populations are collective entities.
• Section 1.2 of parent patent application 09/470,956 describes many limitations and problems of the prior art. Many of these problems, limitations, and solutions are illustrated in the context of clinical trials. This invention also addresses these problems and limitations but generally in the broader context of complex systems. Section 1.2.2 of this document emphasizes problems more specifically addressed by this invention. Section 2.4 of this document describes how this and the parent invention address the following problems.
• the statistical method does have some functionality for analyzing repeated measures data, especially for groups. For example, the statistical method often is used to analyze change scores such as pre-post differences in clinical trials. However, this functionality becomes limited as the number of repeated measurements increases. This limitation is due to the fact that the number of differences between any two measurements increases rapidly with the number of repeated measurements. In addition, it is not meaningful, appropriate or useful to conduct statistical tests on all differences that are possible when there are more than a few repeated measurements.
• the statistical method also includes techniques such as repeated measures analysis of variance.
• techniques such as repeated measures analysis of variance.
• the usefulness of such techniques tends to be limited when the levels of one or more independent variables differ across many repeated measurements for each individual. For example, generally it is not feasible with conventional analyses to substitute blood levels of drug for planned doses before rerunning analyses of the effects of treatment on health.
• the statistical method is best suited for analyses of cross-sectional data for collective entities such as groups. Many statistical descriptions and inferences are about measures of central tendency for groups. Statistical analyses often are based on assignments of individuals to groups such as treated or not treated, responder or non-responder. The results of such analyses apply most directly to collective entities.
• the statistical method often is applied to describe groups and to use sample data to make inferences about populations.
• Statistical inferences are used to draw generalized conclusions and make predictions.
• the extent to which generalized conclusions and predictions about collective entities apply to individuals generally is limited. This can be illustrated in the context of group clinical trials. Individual patients are not apt to experience the same safety and efficacy as the average patient in a clinical trial.
• Science is accounting for more and more factors that affect the responses of patients to medical treatments. For example, advances in genetics are identifying many ways in which individuals differ in manners that are relevant to disease and response to treatment. People need better ways to individualize treatment.
• Some high priority tasks are to identify how the products of gene expression function together and to identify how genetic differences that distinguish individuals, such as single nucleotide polymorhpisms, affect biological functions and responses to treatments.
• Such tasks currently are hampered by a lack of methods that can be applied to individuals to measure how proteins interact to control biological functions, of how treatments affect protein interactions, and of how treatments interact with proteins and health variables. Measurement of such interactions for individuals, as distinct from groups, is becoming increasingly valuable as it becomes easier to identify how individuals differ genetically.
• Group assignments and averages tend to obscure effects of genetic differences on health for individuals and their individual responses to treatments. This makes it difficult to identify genetic differences and form classifications that are predictive of health disorders and differential responses to drugs. This in turn makes it difficult to target drugs to the right patients during drug development and during clinical care.
• a productive but conventional experimental research strategy is to isolate independent variables and investigate their effects one by one. Such research often is hypothesis driven — hypotheses that may be rejected by statistical tests based on group data for collective entities.
• This isolate-and-test strategy tends toward simplified models that do not reveal how many variables, parts, and manifestations of complex systems interact in coordinated manners.
• the failure to measure how complex systems interact in coordinated manners is a problem because coordinated action is a hallmark of how complex systems function in interesting and important ways.
• U. S. Patent No. 6,055,491 involves a method and apparatus for analyzing co- evolving time sequences.
• U. S. Patent No. 6,249,755 and U. S. Patent No. 5,528,516 involve an apparatus and method for event correlation and problem reporting.
• U. S. Patent No. 6,134,510 describes a method for detecting synchronicity between several digital measurement series with the aid of a computer.
• U. S. Patent No. 6,098,024 addresses a system for process data association using LaPlace Everett interpolation.
• U. S. Patent No. 6,051,209 covers a method of evaluating the effects of administering external stimuli or a treatment on the brain using positron emission tomography.
• Section 1.2.2.4 cites two web pages. Section 4.2.4 also quotes the first of these two web pages.
• the two web pages are: Institute for Systems Biology, What is Systems Biology, URL http://www.systemsbiology.org/workwhat.html, Viewed 4/10/01 ; and
• Section 1.2.2.4 also cites the following article:
• FSH follicle-stimulating hormone
• This invention is a method or system to constmct at least one profile representing how the actions of an object of investigation are coordinated, the profile(s) being based on computed measures of longitudinal association or temporal contingency that quantify patterns of interaction in repeated measures or time series data that include two or more variables for one individual.
• profiles are called action coordination profiles (ACPs).
• ACPs can provide quantitative descriptions of how individual complex systems may control and regulate themselves, of how two or more individual systems may interact, of how complex systems may be controlled or affected by their environments including treatments, and of how individual systems may control or affect their environments.
• ACPs are limited to selected variables and episodes of action for particular objects of investigation. This is illustrated by the examples in Section 4.9.
• One example involves certain pituitary and reproductive hormones measured every 5 minutes for up to about 12 hours for individual ewes.
• Another example involves variables considered to affect the Gross Domestic Product of the United States economy using quarterly data for about 42 years.
• Empirical induction involves procedures to draw generalized conclusions and make predictions from data. More specifically, this invention and its parent involve computational procedures to provide high quality generalized conclusions and predictions as high quality is defined in Section 1.2 of the parent application.
• the key innovative concept for this invention and its parent comprises a computational method and system specifically designed to process repeated measures and time series data to measure interactions between and among variables for objects of investigation that are individuals.
• the parent application describes the Method for the Quantitative Analysis of Longitudinal Associations (MQALA).
• MQALA and ACPs include an extensive set of computational tools and analytic options that users can select and apply to address many types of problem encountered in scientific investigations and practical affairs. All these tools and analytic options are based on a core set of computational methods or systems.
• This invention and its parent are distinct from and often complementary to the statistical method. As such, this invention facilitates scientific investigations of individuals both as individuals and as members of collective entities. For example, these inventions often can be used to facilitate both the individualization of medical care and the conduct of group clinical trials for treatments used to control or manage chronic disorders.
• ACPs are a direct extension and distinct improvement on the parent application, much material in the parent application also applies to ACPs. Many terms used in this application are defined in Section 2.9 of the parent application.
• An ACP can be characterized as a set of computed measure values, the set having two dimensions. One dimension represents independent events and a second dimension represents dependent events. Each column or row for the dimension representing independent events corresponds to one of two or more variables or sets of variables or the results of applying certain features used to define independent events. Each column or row for the dimension representing dependent events corresponds to one of two or more variables or sets of variables or the results of applying certain features used to define dependent events.
• Table 1 illustrates the general structure of an ACP with 10 variables and only one column or row for each variable. The same variables are used for both dimensions. Rows represent the variables functioning as independent variables (IVs) to define independent events. Columns represent the variables functioning as dependent variables (DVs) to define dependent events. Cells are formed at intersections of rows and columns. Table 1 uses the symbols "o" and "*" to represent scores or measure values in general. Each cell of Table 1 that contains an "*" indicates that the score or measure value was obtained when the variable labeling a row was functioning to define independent events. Each cell of Table 1 that contains an "o” indicates that the score or measure value was obtained when the variable labeling a column was functioning to define dependent events. There are no measure values for cells on the concordant diagonal, which are represented with the symbol "-”. Additional columns and rows would be used to represent Boolean events defined on two or more variables, to represent transition events, or to represent additional ways of defining independent or dependent events.
• EaGh cell in Table 1 that is identified by an "*" or an "o" represents a particular interaction.
• Particular interactions also can have dimensions. Dimensions for particular interactions represent analysis parameters such as level of the independent variable, level of the dependent variable, delay, persistence, episode length and episode criteria for the independent variable, and episode length and episode criteria for the dependent variable.
• dimension is being used in two contexts. In the context of ACPs, “dimension” refers to variables functioning to define either independent or dependent events. In the context of particular interactions that are represented by cells, “dimension” refers to analysis parameters that may or may not have multiple levels.
• the computed measure values in ACPs generally are either longitudinal association scores or values of measures derived from longitudinal association scores.
• Section 4.4 identifies examples of measures that Gan be used to construct ACPs.
• the magnitude of each score or measure value in ACPs quantifies either the amount of evidence for a longitudinal association or the strength of that association.
• the signs of longitudinal association scores indicate positive or negative associations. Positive scores indicate that dependent events are more apt to occur in the presence of independent events than in the absence of independent events. Negative scores indicate that dependent events are less apt to occur in the presence of independent events than in the absence of independent events. Zero scores or measure values indicate no evidence for longitudinal associations or temporal contingencies.
• each of the variables used to define independent and dependent events for ACPs would need to be measured or assessed repeatedly for an individual on two or more occasions.
• each variable should have the potential to vary - fluctuate in level or recur over time - for the obje ⁇ t of investigation represented by the ACP.
• Variables could be transformed mathematically before computing scores or measure values in ACPs.
• ACPs can be portrayed as tables, figures, graphs, and displays. It is recommended that columns and rows in ACPs for particular types of investigation be presented in standardized orders to facilitate comparisons and analyses of profiles for different- individuals or for different episodes of action.
• events may function in feedback loops to affect more events of the same type.
• neurotransmitters can have both pre- and post-synaptic receptors so that release of a transmitter can help propagate a signal and feed back to affect release of additional transmitter.
• MQALA can be used alone or together with experimental procedures to help distinguish causal from non-causal associations. Some portions of ACPs could remain blank if, for example, investigators determine that it would not be meaningful to consider variables that were under control in experimental investigations to function as dependent variables.
• various analysis parameters would be used to obtain the measure values in ACPs.
• Level of independent variable and level of dependent variable are required analysis parameters when the variables are dimensional (when a series of values for a variable has more than two different values) and the user of MQALA decides to examine more than two levels.
• delay Another analysis parameter, delay, would be a primary analysis parameter when ACPs are used to investigate the temporal criterion of causal and other predictive relationships (Section 4.8.9).
• Delay is defined on variables functioning as independent variables. Users of ACPs could specify one or more particular values of delay or a range of values. One ACP or portion of an ACP would be computed for each particular value of delay. In addition or alternatively, one ACP could summarize scores across a range of values of delay.
• Additional analysis parameters for interactions include episode length and episode criterion for independent and dependent variables as well as persistence defined on variables functioning as IVs. Users can define additional analysis parameters. Typically, the same scoring options would be selected for each IV and for each DV that are used in particular ACPs.
• scores actually shown in ACPs would be summary scores.
• Information about the location of each summary longitudinal association score or derivative measure in an array identifies the conditions that yielded the summary measure. These conditions are defined in terms of features such as analysis parameter levels and Boolean events. Users would be able to drill down from scores shown in summary ACPs to examine scores as functions of analysis parameter levels and in terms of Boolean events.
• ACPs are a way of displaying particular types of quantitative information so that it can be used to discover and describe patterns of longitudinal association or temporal contingency between and among variables and events. Use of ACPs to discover and describe patterns in a systematic, comprehensive, and detailed manner will advance the objectives of scientific investigation, the conduct of practical affairs, and decision-making.
• MQALA can be viewed as a contribution to "temporal contingency analysis.”
• the contingencies (associations) involve independent and dependent events defined in multidimensional spaces formed primarily by applying analysis parameters and Boolean operators to transformations of repeated measures data including time series.
• Independent and dependent events can be defined in great detail.
• Analysis parameters account for things such as levels of independent (predictor) and dependent (predicted) variables.
• Optional analysis parameters account for things such as episodes of events.
• An independent event defined using such parameters and a temporal resolution of one day. Did or did not a given patient meet the criterion on each of a series of days of taking 100 mg or more of a given drug (Drug 1) on 5 out of 7 consecutive days? Additional optional analysis parameters can be used to define temporal aspects (delays and persistencies) of relations between events.
• Boolean operators can be applied to events defined with analysis parameters to define additional events called Boolean events that are based on two or more independent or two or more dependent variables.
• a Boolean independent event could consist of meeting the criterion defined for Drug 1 in the preceding paragraph AND the criterion of taking Drug 2 at a dose of 50 ing or more on 4 out of 6 consecutive days.
• the presence of such a Boolean AND independent event may be sufficient, for example, to increase the presence of a particular type of dependent event such as the level of a liver enzyme being above the upper limit of normal.
• MQALA analyzes such contingencies between independent and dependent events. Many thousands of different events and types of events can be analyzed simultaneously in particular investigations to identify the levels of analysis parameters and the Boolean events that yield the most evidence for associations or interactions.
• temporal contingency analysis indicates that MQALA and any particular ACP quantifies and describes the directions and amount of evidence for contingencies (associations or interactions) between and among events as these contingencies are evident in data that are about the individual and are collected over time from the individual. MQALA also quantifies the strength of associations, contingencies, or interactions. MQALA's capability to analyze temporal contingencies derives from the fact that it is applied to longitudinal, repeated measures, or time-series data as relatively distinct from cross-sectional data.
• ACPs also can be described with coined terms such as “action coordination fingerprints,” “movement coordination fingerprints,” “behavior coordination fingerprints,” and “interaction fingerprints.”
• action coordination fingerprints focuses attention on the fact that ACPs can describe that that is characteristic of individuals that may be unique or different from other individuals.
• ACPs can be used to describe that that is characteristic of episodes of coordinated action, movement, or behavior for individuals. For example, episodes of coordinated locomotion of horses have been characterized as walk, cantor, trot, and gallop.
• fingerprints in its conventional use refers to the form or structure of skin on the fingers.
• ACPs fingerprint something that is more abstract and conceptual — namely the way actions interact. Interactions indicate coordination. ACPs can fingerprint how individuals function, control, and sustain themselves as well as interact with each other and their environments.
• Section 1.2 of this application describes related art in the context of creating, verifying, and refining mathematical models that represent objects in the world. More specifically, the referenced section presents certain limitations and problems related to using the statistical method for this purpose. This section and its subsections describe how MQALA and ACPs help address these limitations and problems.
• MQALA and the statistical method are computational methods of empirical induction, they are distinct in other key respects. These distinctions include the type of data (evidence) that the two methods are best suited to analyze, the objectives of analyses, the computational procedures themselves, and the type of entities about which conclusions are drawn and predictions are made.
• MQALA analyzes repeated measures or time series data for particular individuals. MQALA requires data for at least two variables or types of events. At least one variable must function as an independent variable and at least one variable must function as a dependent variable. Both independent and dependent variables must vary within individuals in order to obtain nonzero longitudinal association or benefit/harm scores.
• the statistical method is best suited to analyze cross- sectional data for groups of individuals.
• Inferential statistical procedures also generally require data for independent and dependent variables from groups with two or more individuals per group.
• MQALA relies on longitudinal associations (temporal contingencies) between and among variables within individuals.
• the statistical method is best suited to analyze cross-sectional associations — differences between and among individuals or groups of individuals.
• MQALA Objectives of analyses conducted with MQALA are to quantify, discover, analyze and describe longitudinal associations (temporal contingencies) between and among variables within individuals.
• MQALA provides generalized conclusions about longitudinal associations between and among variables for individuals. Such conclusions are generalized over repeated measurements. MQALA does this with a variety of scores including scores presented in the form of ACPs.
• MQALA also supports predictions. These predictions are about how individuals will function or respond in the future. Predictions are based on the assumption that past experience can be used to help predict the future.
• MQALA supports predictions in at least two related ways.
• generalized conclusions about how an individual has functioned or responded to date can be used to make predictions about how that individual will respond or function in the future. For example, assume that a benefit/harm score based on many repeated measurements of drug dose and blood pressure for a particular patient over the course of the last year indicates that the drug had a substantial beneficial effect for that patient. This score would support the prediction that the same drug would continue to have the same beneficial effect for the same patient over the course of the next month.
• MQALA also supports predictions with a feature called predictive indices.
• Predictive indices are one way to use information from two or more predictors (IVs) or sets of predictors used to define Boolean events to make predictions about a predicted variable (DV).
• Predictive indices are computed directly from information used to compute particular longitudinal association scores.
• MQALA supports direct predictions. That is, the predictions are for the same individual that the data are about. Furthermore, the predictions are for the same variables analyzed with the same analytic options. MQALA does not directly support inferences from one individual or group of individuals to another. However, MQALA provides scores and other measures that can be analyzed statistically to make such inferences when the scores or measures are available for two or more individuals. This illustrates the complementarity of MQALA and the statistical method.
• MQALA is a powerful new set of computational tools for drawing conclusions and making predictions about individuals by providing quantitative descriptions of experience that has been recorded as repeated measures data.
• the statistical method is best suited to describe characteristics of groups and to quantify, discover, analyze and describe cross- sectional associations between and among variables for groups of individuals.
• the statistical method includes procedures for using group descriptions to make statistical inferences from samples of individuals to populations of individuals.
• Descriptive statistics are best suited to describe groups of individuals. The application of such group descriptions to individuals is indirect. Similarly, statistical inferences generally are for groups rather than for individuals.
• MQALA must convert any dimensional series for independent and dependent variables into sets of dichotomous series. All analysis parameters and Boolean events are defined on such dichotomous series. Dichotomous series for independent variables and dichotomous series for dependent variables are cross-classified to yield 2 x 2 tables. This procedure can easily yield thousands of 2 x 2 tables for any particular individual.
• MQALA continues by computing standardized longitudinal association or benefit/harm scores for each of these 2 x 2 tables. These scores are standardized with respect to all scores that are possible given the marginal frequencies of observed 2 x 2 tables. Standardization allows the scores to be summarized and compared. In addition, standardization makes it reasonable to compute overall benefit/harm scores across many dependent variables for particular individuals. Overall benefit/harm scores can be computed with or without differential weights.
• MQALA draws conclusions and makes predictions about individuals.
• individuals include populations investigated as wholes.
• the statistical method is best suited to draw generalized conclusions and support predictions about groups and populations of individuals.
• 77 MQALA can be used to make generalized conclusions about individuals. Such conclusions are generalized over time within individuals where time is represented by repeated measurements.
• Section 2.6 of the parent patent application discusses differences between direct, indirect, and doubly indirect predictions together with some advantages of using direct predictions for individuals.
• the parent application also explains how measures of longitudinal association, such as those used in ACPs, can be reliable and valid measures of longitudinal association for individuals.
• measures of longitudinal association such as those used in ACPs
• applying experimental procedures within individuals can enhance validity. Collecting and analyzing data from many repeated measurements can increase reliability.
• ACPs can be computed to describe how the parts, variables, and manifestations of unique individuals interact.
• the US economy is a relatively unique individual. It generally is not feasible to investigate unique individuals by sampling populations and making inferences from the samples. For such reasons, MQALA is better suited than the statistical method to investigate individuals that are unique.
• ACPs also can be computed for individuals that may be different — not typical — of average individuals.
• patients with high blood pressure can differ with respect to concurrent disorders, concomitant treatments, gender, race, age and other factors.
• Conventional strategies for investigating treatments favor homogenous groups of substantial size. It can be difficult to recruit samples of substantial size when many factors differentiate patients.
• the number of populations that need to be investigated also increases with the number of factors that differentiate patients.
• the number of populations that need to be investigated and the number of individuals available in each population clearly limit the strategy of investigating homogeneous groups.
• MQALA including ACPs, address such problems by providing unique functionality to help enable scientific investigations of individuals.
• MQALA appears to be the missing key for providing individualized or personalized health care that is for people with chronic health concerns and based on objective scientific procedures for drawing generalized conclusions and making predictions from data. Chronic disorders and their treatments often are investigated best with repeated measures and time series data.
• MQALA Gan be used to help discover how individual differences affect susceptibility to disease and response to therapy. Differences relevant to both include genetic differences.
• MQALA is an important analytic tool is that it helps enable scientific investigations of how individuals interact with their physical and social environments. The uniqueness, richness, and continuity of such interactions appear to be the essence of individual identity.
• MQALA which now includes ACPs, can help address complexity by simultaneously measuring how many variables interact for objects that are individuals.
• the variables can be internal or external to the object.
• the interactions can involve variables both within and across levels in measurement hierarchies.
• the variables can act in different combinations. Any interactions can be positive or negative.
• ACPs are a new way to image complexity as it becomes evident in how individuals function, respond, and act as agents. Images of complexity based on ACPs can help users visualize complexity. Visualizing complexity can help make it understandable.
• Images of functional and response complexity should be distinguished from images of structural complexity.
• Brain scans obtained by Computerized Axial Tomography illustrate structural complexity.
• an ACP of an individual's brain could show how every region of the brain interacts with every other region of the brain.
• Such an ACP which would illustrate functional and response complexity, would be easier to understand if it were obtained under a given set of test conditions.
• ACPs that image functional complexity of individual brains can be computed from a series of functional brain images (Section 4.2.7.2).
• ACP images of functional and response complexity can be very extensive.
• ACPs can have a virtually unlimited number of rows and columns for each dimension.
• an image showing functional interconnectivity of brain regions could have one row and column for each of the corresponding pixels in the series of functional magnetic resonance images from which it is computed. Additional levels could be added for Boolean events.
• MQALA including ACPs
• ACPs ACPs
• MQALA is an important advance in scientific methodology and for the application of technology to achieve human objectives because ACPs can help users visualize functional and response complexity; facilitate the creation of mathematical models of how complex systems function, respond, and act; as well as the creation of artificial systems that learn.
• MQALA converts dimensional series into sets of dichotomous series using integrated scales as described in Section 4.1.2 and illustrated in Tables 6 and 7 of the parent patent application.
• the values of measures portrayed in ACPs are computed from cross-classifications of independent and dependent events as defined on such dichotomous series as illustrated for longitudinal association scores in Section 4.1.1 of the parent application. As such, the computational procedures do not assume that effects of independent variables or events on dependent variables or events are proportional to independent variable levels.
• MQALA can use Boolean independent events and Boolean dependent events to help determine if particular combinations of events are associated more with other events than with the same variables considered individually. This use of
• Boolean events described in Section 4.1.1 1 and illustrated in Table 17 of the parent application, addresses the problem of non-additivity as described in Section 1.2.2.3 of this application.
• Section 1.2.1.2.1.2 and its subsections in the parent application describe a number of problems involving comprehensiveness and detail in the context of evaluating treatments for health disorders. Section 2.7.1.2.1.2 and its subsections in the parent application describe how MQALA helps address these problems. Furthermore, Section 4.2.1.1 of the parent application includes information about how MQALA addresses problems involving the emergence of system properties and unique entities — problems that are included in Section 1.2.1 also of the parent application.
• ACPs further address the problems involving comprehensiveness as described in Section 1.2.2.4 of this application because of the capability of ACPs to provide quantitative displays of large numbers of interactions simultaneously. Measurement of the interactions for an individual effectively converts the interactions into a multidimensional object that can be visualized, graphed, and subjected to established quantitative methods such as those of morphometrics.
• MQALA including ACPs
• ACPs ACPs
• MQALA is an advance in data processing that helps make systems biology and discovery science possible.
• MQALA can be applied to many types of complex system in addition to biological systems. This flexibility of application derives from the fact that MQALA can be applied to data for various types of entity much as the statistical method can be applied to data for various types of entity.
• a fundamental distinction between the two methods is that MQALA is specifically designed for application to individual entities while the statistical method is best suited for collective entities.
• ACPs and MQALA can help address the need for detailed investigations.
• ACPs can address interactions between and among a virtually unlimited number of variables. This can, for example, help users avoid problems that can result when investigators lump many health variables into a single health measure.
• each score in an ACP typically would be a score that summarizes a multidimensional array of scores.
• the dimensions of arrays correspond to analysis parameters such as IV level, DV level, episode length, episode criterion, and persistence. Users of ACPs and MQALA would be able to drill down into these arrays to examine in great detail the interactions shown in ACPs.
• MQALA is an integrated set of data processing tools built around core computational measurement methods and providing users with many options about how to proceed. These core computational methods measure longitudinal associations between and among variables and events for individual entities that are complex systems. As such, MQALA addresses all of the five problem areas - individuality, complexity, nonlinearity, comprehensiveness, and detail — as a set.
• Section 4.8.8 describes how ACPs can be used to measure interactions involving different types of action.
• Section 4.8.1 1 describes use of ACPs to investigate nested systems.
• Section 4.8.12 describes use of ACPs to distinguish episodes of action.
• Section 4.8.4 describes how ACPs can be used to fingerprint individuals, fingerprints that can be used to develop classifications of individuals into groups that are more homogeneous with respect to how they function and interact with their environments.
• Such uses provide tools both for partitioning the subject matter of science and for examining how the parts interact to form complex systems.
• MQALA is based on a tenet that is central to science: If you want to investigate something scientifically, measure it! MQALA measures the interactions between and among variables and events that help reveal how complex individual systems of many types function, regulate, and sustain themselves as well as how they respond to and act upon their environments.
• FIG 1 illustrates steps to construct ACPs.
• Figure 2 portrays average strength of a longitudinal association measure as a function of delay for interactions involving GnRH and P-LH
• Figure 3 portrays average strength of a longitudinal association measure as a function of delay for interactions involving GnRH and J-LH.
• This invention is a platform data processing technology that applies to various types of objects and actions, has various computational features, and many uses.
• ACPs This section provides a detailed description of ACPs.
• the first major subsection illustrates types of objects that can be represented by ACPs. Objects are distinguished from their environments.
• the next major subsection illustrates types of action that can be manifested by and upon various objects of investigation.
• Several measurement technologies yield data that are particularly well suited for processing to yield ACPs.
• Major components of established sciences and disciplines can be investigated with ACPs.
• Additional major subsections illustrate uses of ACPs, cover databases that include ACPs, and statistical analyses of two or more ACPs.
• the final major subsection presents several examples of ACPs.
• ACPs can be used to investigate objects that are complex systems of many types. Systems are considered to be complex when they have many parts, variables, or manifestations that can interact. A system also is considered to be complex when many things in its environment can act upon it. Here are some examples of complex systems that can be investigated with ACPs. These examples are not exhaustive or mutually exclusive. 4.1.1. Organisms Including Persons
• Organisms have been defined as a complex structure of interdependent and subordinate elements whose relations and properties are largely determined by their function in the whole. Organisms carry on the activities of life. Organisms are considered to include persons.
• Portions of organisms include cells, tissues, organs, systems, biochemical pathways, and biopathways. Such portions of organisms have differentiated structures or functions.
• Other examples of man made systems include healthcare, banking, and software systems whose behavior can be monitored with time series data.
• ACPs can be used to investigate reciprocal interactions of objects involving two or more individuals that may interact to form compound objects. This can be illustrated with episodes of dance in which the object is a pair of dancers and the actions are the movements of both dancers (Section 4.2.1). ACPs can be used to describe the nature and extent to which movements of dancers are coordinated.
• compound objects need not be of the same type. For example, a horse and a rider or a man and a machine form compound objects.
• Section 2.6 of the parent application describes how MQALA can be applied to objects that are populations investigated as wholes. Investigating populations as wholes is distinct from making inferences about populations from samples of populations' individuals.
• the section just referenced illustrates the research strategy of investigating populations as wholes in the context of epidemiology.
• This strategy can, for example, use environmental variables such as measures of air pollution that are considered to affect whole populations together with population variables such as rates of death or hospital admission.
• ACPs for populations considered as wholes could be based for many variables considered to affect populations as wholes and various population rates and proportions.
• Inclusiveness involves physical, functional, and conceptual boundaries identified or set by investigators to help distinguish that which is within systems from that which is part of systems' environments. Thus, for example, investigators of biological systems often distinguish endogenous from exogenous substances.
• a nested system in the context of investigations of living things in which each successive individual can be considered to be nested within the next level: cell, organ, organism, and social system.
• Another example in the context of biological systems is neuron, brain, and organism.
• the company's stock price can be investigated in terms of its own periodic measures of business performance. Then the stock's price could be investigated in terms of various stock sectors including the sector of which the stock of interest is a member. Then the price of a broad range of stocks across many sectors could be investigated in terms of other potential investments in a particular country's economy such as bonds, real estate, commodities, and collectibles. Finally the performance of a particular country could be investigated in the context of periodic measures of how other countries perfor .
• Activity at each level of a nested system can be expected to have effects on the activity of included component individuals.
• the performance of the economy of the United States in the world economy could be expected to have effects on stock prices for individual companies.
• individual companies' performances could be expected to have effects as agents on more inclusive levels in nested systems.
• the performance of a major United States company could be expected to have some impact on the performance of the economy of the United States in the environment of the world economy.
• ACPs can be used to investigate reciprocal interactions between individuals and their environments when the systems are defined to have the different degrees of inclusiveness that are characteristic of nested systems. This can be done when measures of action for one component of a nested system become variables for computing ACPs for other more or less inclusive components of the nested system.
• Predictive indices can be an efficient and operational way to summarize the effects of many independent variables or predictors so that many types of action can be summarized in one variable that can in turn be used for computing ACPs for nested systems.
• ACPs can be computed for systems that can be investigated with time series data for variables that fluctuate in level or recur over time. Examples include sodal systems, ecosystems, and weather systems.
• ACPs manifest actions of many types.
• the following subsections illustrate various types of action that can be measured to yield data that are processed to construct ACPs.
• Resulting ACPs go beyond levels of variables from which they are computed to reveal how variables interact to form functioning systems.
• Procedures for obtaining data that are to be processed to construct ACPs can be specified in protocols.
• Measurement technologies here considered as they measure various types of action, are improving rapidly, often in several ways simultaneously. Here are examples of different ways in which measurement technologies are improving.
• New measurement technologies are measuring things that were never measured before. For example, modern imaging technologies can measure brain activity such as areas of the brain that are activated while performing mental arithmetic. Rating scales are being developed to operationalize concepts at high levels of measurement hierarchies, concepts such as health-related quality of life.
• Measurement technologies are becoming more sensitive. For example, they can detect and measure concentrations of parts per billion or less.
• Some measurement technologies can measure thousands of variables simultaneously. Microarrays that are used in biotechnology can measure thousands of variables on one chip. Some measurement technologies are achieving high temporal resolution. Temporal resolution refers to the number of repeated measurements per unit time. Some processes can be measured repeatedly to yield time series data with many measurements per second.
• Measurement devices are being miniaturized.
• MicroElectroMechanical Systems (MEMS) for sensing flow, pressure, and chemicals are being developed that can operate from within the human body.
• Measurement devices are being Internet enabled. For example, Internet enabled medical monitoring devices can report health variables from almost anywhere.
• This invention is a computational method and system.
• the input for this invention can comprise much output from conventional measurement technologies.
• the output from this invention comprises quantitative descriptions of how complex individual systems function. As such, this invention increases the value of many conventional measurement technologies.
• ACPs were inspired by the task of analyzing data collected by motion capture technology.
• One such technology that can be applied to humans uses 36 reflective markers on various parts of the body together with five video cameras to capture three dimensional motion data.
• Data can be captured during movement protocols that capture episodes of movement such as walking or a short series of repeated golf swings.
• Software that is part of the motion capture technology computes values of many variables comprising measures of linear and angular velocity and acceleration.
• Motion capture technology can result in extensive databases of high temporal resolution data with thousands of variables. Such databases are descriptive of particular episodes of movement for particular individuals.
• ACPs can be computed from such databases. Such ACPs can show how every variable interacts with every other variable. ACPs for episodes of different behavior and for different individuals would be expected to have characteristics that could be assessed with morphometric analyses to identify salient features that distinguish various ACPs.
• ACPs for movement will be used to illustrate coordination.
• a particular movement protocol calls for a series of three repeated golf swings. It is anticipated that an expert golfer would have a well-defined ACP with many large magnitude scores. Such scores would indicate that particular movements are highly coordinated with other movements. Scores at specific values of delay would indicate precise sequences of movement. In contrast, the ACP of a novice golfer under the same conditions would be anticipated to be relatively flat. Movement sequences would not be as specific and repeatable.
• ACPs also could be used to picture the effects of various interventions. Consider again the ACP of an expert golfer. Intoxication with a drug that alters behavior could be expected to flatten such a profile.
• ACPs can be used to investigate coordination for actions in addition to movement. 4.2.2. Physical and Electromagnetic Action
• Physical and electromagnetic action involves properties that are part of the subject matter of physics. Included would be measures of pressure, flow, wavelength, intensity, and voltage. Some of the most interesting properties of complex systems appear to emerge from how basic physical actions are organized and interact.
• Biological activity often is monitored with technologies such as electroencephalography and electrocardiography. Such procedures can yield many variables in the frequency and spatial domains. Such variables can be analyzed with ACPs.
• Changing levels or concentrations of chemicals can be measured repeatedly and may indicate chemical reactions or interactions.
• ACPs can be used to help investigate how such actions may be coordinated.
• Biochemical reactions underlie much of the life sciences and medicine.
• Biologically active substances include endogenous products of gene expression such as hormones, neurotransmitters, receptors, and messenger molecules, as well as exogenous substances such as drugs and other chemical exposures.
• endogenous products of gene expression such as hormones, neurotransmitters, receptors, and messenger molecules, as well as exogenous substances such as drugs and other chemical exposures.
• exogenous substances such as drugs and other chemical exposures.
• Many of the laboratory measures used in medicine reflect biochemical action.
• Bio action becomes evident in living things. Although biological action includes physical and biochemical manifestations of living things, a new, more inclusive view of biological action is emerging. This is illustrated by the web site for the Institute of Systems Biology, "systems biology studies the complex interaction of all levels of biological information: DNA, mRNA, proteins. functional proteins, informational pathways and informational networks to understand how they work together.”
• the systems approach to biology can be contrasted with the more conventional approach that tends to investigate components of biological systems one by one.
• the systems approach explicitly recognizes that some of the most interesting and important properties of biological systems, such as immunity, are emergent or systems properties.
• the systems approach to biology also can be applied to portions of biological systems.
• attention, memory and learning can be considered as emergent or systems properties for brains or nervous systems.
• MQALA can be applied most directly to physiological and environmental variables that can fluctuate in level over time and events that can recur for individuals. Although MQALA is not well suited to investigate structures themselves, MQALA can be applied to investigate how structures function.
• Section 4.2.6 of the parent patent application includes major elements of an analysis of classical conditioning, operant conditioning, paired-associate learning, and associative learning in terms of changes in one type of longitudinal association in the presence of another type of longitudinal association.
• Section 4.2.6.4 of the parent application illustrates how such analyses can be applied to create artificial systems that learn.
• Artificial learning systems or robots can be considered as embodied models of learning systems.
• New measurement technologies tend to yield more data than people have been able to understand. This invention can help process much of this data to facilitate understanding of how complex systems function and interact. As such, this invention can increase the value of these measurement technologies by serving human needs.
• New developments in biochemical measurement technology include microarrays or Ghips that can measure thousands of products of gene expression at one time. Any of these gene products may interact with any other gene product - interactions that may indicate up or down regulation of biochemical pathways or biopathways. Huge numbers of interactions can be investigated simultaneously when such chips and related procedures can be applied repeatedly and the resulting data are used to construct ACPs.
• ACPs can be used to help quantify, discover, and describe protein-protein interactions. Quantification of protein-protein interactions would help make life sciences more productive. Discoveries and detailed descriptions made with ACPs could be objects of patent applications that identify biochemical pathways that may be new targets for drug development.
• ACPs can be used to analyze protein-protein interactions.
• Section 4.8.9.2 describes how ACPs can be used alone and together with experimental procedures to help investigate the temporal criterion of causal and other predictive associations.
• Interactions can be analyzed as functions of analysis parameters used to construct ACPs.
• Interactions can be analyzed when operators such as AND, OR, NOR, XOR and NOT are applied to sets of independent and dependent events to define Boolean events. Such analyses can contribute to a detailed understanding of how organisms function.
• Exogenous substances can be used as experimental probes to investigate biological systems.
• ACPs ACPs
• the first of these strategies would involve administration of the experimental probe repeatedly, possibly at various levels or doses, while many biochemicals are being measured at the same time.
• the resulting data would be used to construct ACPs that include the probe as a variable. This strategy would help reveal how the probe may interact with all the biochemicals.
• the second of these two research strategies involves constructing ACPs under different treatment conditions, preferably for the same individuals. For example, separate ACPs can be constructed for the "on treatment” and the "off treatment” conditions. Differences in such ACPs would help reveal how treatment may affect any of many interactions portrayed in such ACPs. Unlike the first strategy, treatment would not be included as a variable in ACPs constructed with the second strategy.
• the two research strategies just described are fundamentally different.
• the first strategy would reveal how treatment may affect levels of all the biochemicals.
• the first strategy could help reveal how treatment may affect insulin and glucose levels.
• the second of these strategies would help reveal how treatment may affect any of the interactions between or among the biochemicals. For example, treatment may help restore a disordered interaction between insulin and glucose levels. Both research strategies could facilitate drug development by quantifying interactions that may indicate efficacy or possible safety concerns.
• Functional magnetic resonance imaging and Positron Emission Tomography can measure biological activity in, for example, brains.
• Section 4.2.4 of the parent application explains how data from a series of such aligned images can be used to form serial pixel or serial region of interest variables.
• One such variable would correspond to each pixel or region of interest.
• Regions of interest Gould be delimited on the basis or shared structural or functional characteristics.
• Corresponding computational procedures could be applied to form serial voxel (volume element) variables.
• ACPs can be computed using serial pixel, serial voxel, or serial region of interest variables and presented graphically as images. Such images would show how activity in each pixel, voxel, or region of interest interacts with activity in every other pixel or region of interest that is included in the ACP. Section 4.5 includes more information about portrayal of images and displays based on ACPs constructed from functional imaging data. Functional connectivity within brains that is revealed by ACPs and derivative images could be investigated experimentally and non-invasively with transcranial magnetic stimulation of particular brain regions.
• MEMS MicroElectroMechanical Systems
• MEMS are super-miniaturized machines that can perform combinations of sensing, processing, communicating, and acting upon information.
• so-called lab-on-a-chip MEMS are being developed that can operate from within human bodies.
• MEMS will vastly increase amounts of time series data that can be analyzed with ACPs. This includes analyses that can be implemented within MEMS themselves.
• Psychometrics involves mental measurement. Psychophysics is concerned about the effects of physical processes such as wavelength and intensity on mental processes of organisms. Measurement technologies in these areas, many of which are computerized, help make the subject matter of psychology amenable to scientific investigations.
• Performance is measured in terms of things such as speed, accuracy, and skill. Computerized performance measurement technologies often are particularly well suited for yielding data that can be analyzed with ACPs.
• Rating scales and surveys that often are based on self-report are benefiting from new measurement technologies that improve reliability, validity, and ease of use for many measures. For example, measures of health related quality of life are being developed that help provide a common metric for evaluating the effects of many treatment across many disorders.
• Major sciences and disciplines such as chemistry, biology, psychology, sociology, economics, and medicine can be considered to focus on certain combinations or clusters of objects and actions.
• psychology tends to focus on behavior of individual organisms.
• Medicine tends to focus on biochemical and behavioral signs and symptoms of individual organisms with health disorders.
• MQALA Various computational features of MQALA can be used to define independent and dependent events. These events are determined to be present or absent over different times thus forming dichotomous series or sets of dichotomous series used to compute ACPs. Table 1 lists section numbers of the parent application that describe these selected features. Section 4.4 of the parent application illustrates most of these features with an example. Table 1. Computational features by section number in parent application.
• Variable level is a required analysis parameter when data for a variable are a dimensional series and users of MQALA select to investigate more than two levels of the variable.
• Episode length and episode criterion are optional analysis parameters that can be applied to define independent and/or dependent events.
• Delay and persistence are optional analysis parameters that can be applied to define independent events.
• Boolean events and transition events can be applied to define independent events, dependent events, or both. Boolean events and transition events add columns and rows to dimensions of ACPs.
• Parent application describes various measures that can be portrayed in ACPs.
• Parent application Section 4.1.1 describes how longitudinal association scores are computed.
• Parent application Section 4.1.5 describes how longitudinal association scores can be converted to benefit/harm scores for evaluation research.
• Parent application Section 4.1.6 describes computation of three related measures of strength of longitudinal association.
• Parent application Section 4.1.3 describes how longitudinal association scores and benefit/harm scores can be summarized. Analogous procedures can be used to summarize strength of longitudinal association measures.
• ACPs or portions of ACPs can be portrayed with various types of tables, figures, graphs, images, displays, or interactive displays. Section 4.9 of this application gives several examples of ACPs and portions of ACPs that are portrayed as tables. Such tables also can be portrayed graphically.
• ACPs as images and displays will be illustrated in the context of functional imaging as described in Section 4.2.7.2.
• Such images and displays Gould be used to investigate functional connectivity of brain regions in ways that are useful for research and diagnosis.
• These images and displays could be color coded so that one range of colors represents degrees of excitatory activity (positive scores) and another range of colors represents degrees of inhibitory activity (negative scores).
• ACPs derived from series of functional images can portray vast amounts of information. This is true, for example, when ACPs are computed from pixel or voxel data with one variable for each pixel or voxel. Such ACPs could have one row or column for each pixel or voxel functioning as an independent variable and one row or column for each pixel or voxel functioning as a dependent variable. However, such ACPs would not portray information in accordance with familiar anatomical shapes.
• Functional connectivity of brain regions could be portrayed in accordance with familiar anatomical shapes by imaging or displaying ACP information one row or column at a time. This would yield a type of derivative image in which a particular pixel, voxel, or region of interest could be blank while measure values for all other cells in a column or row could be shown with color coding in there anatomically correct positions. Resulting images for particular brain regions would image functional connectivity of the particular regions with respect to all other regions included in the images.
• the type of image just described could be portrayed in an interactive format.
• a cross-hair type pointer on a computerized image to identify a particular brain region. Once identified, the image would show how action in the particular region is associated with activity in every other region.
• the required scores could either be stored in a potentially very large ACP or the required scores could be computed on demand.
• ACPs or portions of ACPs can be stored in databases, with or without additional data. Section 4.7 describes some ways in which these databases can be used.
• ACPs are sets of measurements that can serve as input or be operated upon by statistics and other quantitative methods. Quantitative methods can be applied to individual ACPs, two or more ACPs for an individual, ACPs for two or more individuals, or portions of ACPs.
• ACPs can be said to give shape to interactions. Shapes can be measured with morphometry.
• ACPs can be analyzed statistically. For example, two or more ACPs with corresponding structures can be averaged. ACPs from two or more individuals can be used to describe groups or to make inferences about populations.
• MQALA including ACPs
• ACPs have many uses in addition to applications to various objects, actions, subject matters, and combinations thereof.
• the following subsections identify and describe some such uses. Together, such applications and uses form an extensive and varied tool kit for addressing problems involving repeated measures and time series data. Many problems are best addressed with time series data alone or in combination with cross-sectional data.
• ACPs that are constructed with variables that include two or more variables considered to be internal to the object represented by the ACP can be said to include measures of internal control.
• Terms used to describe internal control vary by scientific discipline. For example, some investigators speak of regulatory control in biological systems. In this context, the variables may measure endogenous substances such as hormones, neurotransmitters, messenger molecules, or other components of biochemical or signaling pathways. Internal control includes biochemical pathways or biopathways. Psychologists may use terms such as self-control.
• ACPs that are constructed with variables that include at least one variable in the environment of the object that is being represented by the ACP can be said to include measures of response to the environment. More specifically, those portions of such ACPs where environmental variable(s) function as independent variables portray responses to environments.
• Treatments are an important subclass of environmental variables. Portions of ACPs that are responses to treatments generally would be portrayed with benefit/harm scores.
• Portions of ACPs corresponding to environmental variables functioning as dependent variables can be said to measure actions of objects represented by ACPs on their environments. In such cases, objects can be said to be functioning as agents acting on their environments.
• test results can be descriptions of how their objects function and interact with their environments. Test results can be presented for many types of independent and dependent events simultaneously. Abnormal results on such tests could be diagnostic of disorders such as health disorders.
• Test conditions could be specified in test protocols. Test protocols would need to specify things such as type of object that is tested, variables used to construct ACPs and how they are to be measured, how any environmental variables would be controlled, all scoring features used to transform data and define events, as well as the type of measures portrayed in ACPs.
• ACPs that result from tests could be accumulated in databases that include ACPs from many individuals. These databases could be analyzed to help distinguish normal from disordered test results and to classify individuals with distinct or disordered test results.
• Section 4.8.5 describes how ACPs can be used to portray test results that can in turn be analyzed to develop classifications of abnormal test results that indicate disordered functioning. Such classifications can be used to help identify predictors of disorder. For example, this approach could be used to search for single nucleotide polymorphisms that are predictive of abnormalities in the regulation of glucose metabolism.
• ACPs that are based on multiple treatment and response variables can provide detailed descriptions of responses to treatments.
• Such ACPs for groups of patients can be used both to help classify responses to treatment and to help identify predictors of differential response. For example, this approach could be used to search for single nucleotide polymorphisms that are predictive of differential responses to drugs.
• Section 4.2 of this application illustrates several broad categories of action that can be investigated with ACPs.
• ACPs that are based on variables that measure different types of action can be used to investigate interactions involving actions of different types.
• Biochemistry is at the interface of chemistry and biology.
• Psychophysics is at the interface between physics and the psychology of perception.
• Social psychology is at the interface between behavioral and social action.
• Section 1.2.2.2 illustrated hierarchies of measurement in the context of complexity.
• ACPs can be used to investigate interactions involving measures at different levels.
• ACPs could be used to investigate interactions involving laboratory measures used in medicine and self-reported symptoms.
• ACPs are quantitative descriptions of associations. ACPs generally portray how each type of event, defined on variables, is associated with every other type of event defined on the variables. Associations often do not indicate causal relationships.
• MQALA and ACPs can be used to explore for causal relationships.
• MQALA and ACPs can be used together with the experimental method to help confirm causal relationships. Both strategies will be discussed in turn.
• MQALA and ACPs can use the analysis parameter called delay to help evaluate the temporal criterion of causal and other predictive relationships.
• Delay is defined on variables functioning as independent variables. It is more feasible to investigate delay with time series data in which measurements are obtained periodically after equal units of time.
• the default value of delay is 0.
• delay is set equal to 0
• associations involve dependent events occurring one time unit after independent events. Users generally would be able to select additional integer values of delay.
• the upper and lower portions of ACPs as indicated by the symbols "o" and “*" respectively in Table 1 , will not be symmetric, especially with nonzero values of delay.
• Asymmetry tables or graphs which are computed from ACPs, can be used to investigate the temporal criterion.
• One way to construct an asymmetry table or graph is to subtract values from one portion of an ACP from corresponding values in the other portion of the ACP.
• Asymmetry with respect to, for example, the cell corresponding to IV 6 and DV 3 can be examined by subtracting the value in this cell from the value in the cell corresponding to IV 3 and DV 6.
• ACPs and asymmetry tables or graphs are useful tools for exploratory data analyses that involve causal or other predictive associations. This type of exploratory data analysis or data mining can be conducted using ACPs that summarize across a range of nonzero positive values of delay. Such ACPs show how each variable may be associated to each other variable in a single ACP or asymmetry table or graph.
• temporal criterion of causal or other predictive associations can be investigated using a series of ACPs and asymmetry tables or graphs that summarize and quantify associations as functions of various values of delay.
• a series of delay specific ACPs and asymmetry tables or graphs can be used to investigate cascades of events in complex systems. For example one variable may be associated with increases or decreases in a second variable after some delay. In turn increases or decreases in the second variable may be associated with increases or decreases in a third variable after some additional delay. In general, more delayed effects are apt to be weaker because they are mediated by a series of more immediate effects that may dampen more delayed effects. It also appears that ACPs and asymmetry tables can be used to investigate positive and negative feedback loops.
• MQALA is entirely compatible with such strategies and procedures when they are applied during data collection.
• MQALA often enables new ways of applying such strategies and procedures by expanding the range of types of data that can be analyzed effectively by computational procedures.
• MQALA often helps make it feasible to randomize doses of treatment to different time periods for individual patients.
• two or more dose levels (which may or may not include a placebo dose of zero) can be used for each patient.
• the conventional experimental strategy that often is applied in conjunction with the statistical method is to assign individuals to separate groups such as treated and untreated patients.
• An alternative strategy that is facilitated by MQALA is to control variable input signals for individuals to examine how a host of other variables interact with the signal. ACPs for two or more individual subjects could then be analyzed statistically.
• Section 4.2.1.2 of the parent application describes the application of MQALA to data mining.
• Section 1.1 of this application describes the interactions that ACPs quantify as becoming evident as patterns in repeated measures and time series data. ACPs can be used to mine for such patterns.
• ACPs can be a major tool for discovery science as described in Section 1.2.2.4. 4.8.11. Use of ACPs for Nested Systems
• Section 4.1.7 illustrates nested systems and how they can be investigated with ACPs.
• ACPs can be important tools for investigating interactions involving physical or conceptual entities with different degrees of inclusiveness.
• Walk, cantor, trot, and gallop illustrate episodes of locomotion for horses. Similarly, episodes can be distinguished for different types of action. MQALA includes tools for helping to distinguish episodes of action.
• Section 4.1.15 of the parent application describes sequential analysis of longitudinal association scores and strength of longitudinal association measures. Sequential analysis also can be called iterative analysis.
• Sequential or iterative analysis also can be applied to ACPs. With this procedure, ACPs would be constructed sequentially over measurement occasions. Measure values for corresponding locations in ACPs can be plotted as functions of time or measurement occasion. Such graphs could be analyzed by looking for inflections or sets of inflections to help distinguish different episodes.
• ACPs can be used to inform the model development process.
• ACPs also can be used to test dynamic models by comparing ACPs produced by models with ACPs for the objects and actions modeled.
• Empirical induction was defined as involving drawing generalized conclusions and making predictions from data. As such, drawing generalized conclusions and making predictions are inventive and important uses of ACPs.
• Section 1.2 of the parent application presents four criteria for high quality generalized conclusions and predictions.
• ACPs can be used as tools to help draw high quality generalized conclusions and to make high quality predictions.
• MQALA including ACPs
• Section 2.8.2 of the parent application describes how MQALA can be used from a practical perspective in the context of decision-making. Similar arguments apply to this improvement on MQALA, ACPs.
• Tables 2 through 5 and Figures 2 and 3 are based on data from reproductive endocrinology. For these tables and figures, the objects of investigation are ewes, the actions involve hormones, and the use is to investigate internal control.
• the data for Tables 2 through 5 and Figures 2 and 3 are described and reported in two publications: Padmanabhan, V., McFadden, K, Mauger, D.T., Karsch, F.J., and Midgley, A.R. (1997). Neuroendocrine control of follicle-stimulating hormone (FSH) secretion. 1. Direct evidence for separate episodic and basal components of FSH secretion.
• FSH follicle-stimulating hormone
• Tables 2 through 4 are based on data for one ewe. Data for these tables were obtained by assessing five hormone measures every 5 minutes for about 12 hours — 143 repeated measurements of each measure.
• the hormone measures are gonadotropin releasing hormone (GnRH), portal luteinizing hormone (P-LH), jugular luteinizing hormone, portal follicle stimulating hormone (P-FSH), and jugular follicle stimulating hormone (P-FSH).
• GnRH gonadotropin releasing hormone
• P-LH portal luteinizing hormone
• P-FSH portal follicle stimulating hormone
• P-FSH jugular follicle stimulating hormone
• Table 2 and Table 4 each portray an ACP.
• the scoring protocol for Tables 2 and 4 is, in brief, as follows. The set of dichotomous series for each variable was formed by first computing the standardized residuals from its linear regression line on time or measurement number. Next, intervals of z-scores for the residuals were used to form 12 dichotomous series for each variable.
• Tables 2 and 4 used optional values of additional analysis parameters. Ten combinations of episode length and episode criterion were applied to variables functioning as independent variables and to variables functioning as dependent variables. These 10 combinations resulted from applying episode length values 1 through 4 and all values of episode criterion that are possible given these values of episode length. Delay values of 0, 1, and 2 and persistence values of 1 and 2 were applied whenever a variable functioned as an independent variable. Table 1 identifies sections of the parent application that describe these analysis parameters. Table 2 portrays summary longitudinal association scores. Table 2 includes summaries of three delay-specific portions of an ACP plus a summary of the entire ACP. The summary of the entire ACP is summarized across the three delay- specific portions of the ACP.
• Each score in the delay specific sections of Table 2 is summarized across 28,800 delay specific longitudinal association scores.
• the number 28,800 is the product of 12 levels of the independent variable, 12 levels of the dependent variable, 10 combinations of episode length and criterion for the independent variable, 10 combinations of episode length and criterion for the dependent variable, and 2 levels of persistence.
• Each summary score in the summary ACP portion of Table 2 is summarized across 86,400 scores (3 times the 28,800 delay-specific scores).
• Table 2 is a particular portrayal of the entire ACP constructed from the hormone data and with the scoring protocol described earlier in this section.
• the entire ACP includes 1,728,000 longitudinal association scores — 86,400 for each of the 20 interactions.
• Each of 20 interactions is evaluated with respect to 8 dimensions that correspond to analysis parameters.
• the first score in Table 2 the summary score for the interaction between GnRH functioning as the independent variable and P-LH functioning as the dependent variable, is 76.728. This score is one score from a distribution of scores with a mean of zero and a standard deviation of 1. The distribution consists of all 47 scores that are possible given the marginal frequencies of the 2 x 2 table from which the score was computed.
• the 2 2 table resulted from the Gross- classification of a particular member of the set of dichotomous series representing the independent variable with a particular member of the set of dichotomous series representing the dependent variable.
• Table 3 indicates that there is much evidence for a longitudinal association between GnRH and P-LH. Furthermore, the association is positive — high levels of GnRH are associated with high levels of P-LH. Section 4.1.1 of the parent application shows how cells of the 2 x 2 table are labeled and how longitudinal association scores are computed.
• Each interaction portrayed in Table 2 could be investigated in detail by examining all or subsets of the longitudinal association scores that are summarized. This includes examination of interactions as functions of any or all of the analysis parameter levels used in the analysis. The location of the summary score in the array of scores that was summarized identifies levels of all analysis parameters that yield the most evidence for the longitudinal association.
• Table 4 is the same as Table 2 except that Table 4 portrays values of summary strength of longitudinal association measures rather than summary longitudinal association scores.
• the strength measure used in Table 4 is the measure labeled So in Section 4.1.6 of the parent application. Section 4.1.6 of the parent application also describes computation of values of the strength of longitudinal association measures.
• Table 4 portrays a different ACP than that portrayed in Table 2 in that the two ACPs use different measures of longitudinal association or temporal contingency.
• Table 4 A portrayal of the ACP for the hormone data that uses values of a summary strength of longitudinal association measure.
• Table 5 demonstrates a statistical analysis of a summary measure of strength of longitudinal association.
• the values in Table 5 are for four associations and six ewes. Labels such as "GnRH to P-LH" indicate that GnRH is functioning as the independent variable and P-LH is functioning as the dependent variable.
• Table 5 includes the value of the first strength of longitudinal association measure in Table 4. It is for Ewe 3 and is shown in bold in Table 5.
• Figures 2 and 3 also are based on the hormone data. These figures demonstrate the use of ACPs to evaluate the temporal criterion of causal and other predictive relationships. Both figures show average values of a summary strength of longitudinal association measure in which the averages are obtained across the six ewes identified in Table 5. The group average value of a summary strength of longitudinal association measure is shown as functions of the analysis parameter called delay.
• Figure 2 portrays associations involving GnRH and P-LH in which both hormone measures function as both independent and dependent variables.
• the key that identifies lines in Figure 2 includes reference to "Extensive” and “Limited.” These labels refer to scoring options used in two scoring protocols. Scores based on the protocol labeled "Extensive” were the same as those described in paragraphs 3 and 4 of this section except for additional values of delay (a total of 7 values of delay, 0 through 6). In contrast, scores labeled "Limited” are based on a protocol that used default values for episode length, episode criterion, and persistence.
• Figure 2 helps validate a new methodology (MQALA and ACPs) by confirming that the new methodology reveals a known relationship — namely that GnRH secretion largely controls P-LH secretion.
• Figure 2 shows clear patterns in average strength of longitudinal associations between GnRH and P-LH as a function of delay. The most extreme values are positive and are for delay equals zero. Furthermore, the curves for the P-LH to GnRH interactions appear to be shifted to the left relative to the corresponding GnRH to P-LH interactions. Together, these results suggest that GnRH secretion tends to elicit P-LH secretion in a manner that is relatively rapid compared to the temporal resolution of these data - the temporal resolution being 5 minutes.
• Results for delay 4 are among the most extreme negative average summary strength of longitudinal association measure values shown in Figure 2. This indicates that high levels of the independent variable are associated with low levels of the dependent variable about 20 minutes latter. The interactions portrayed in Figure 2 tend to be periodic.
• Figure 2 also shows that values of the average summary strength of longitudinal association measure that are obtained with the "extensive" scoring protocol options are more extreme (closer to the maximum values of plus or minus 1) than corresponding values obtained with the "limited” options. This suggests that the optional levels of the analysis parameters called episode length, episode criterion, and persistence do account for additional systematic variation in the data.
• Figure 3 was obtained in the same manner as Figure 2 except that luteinizing hormone was measured in jugular blood rather than portal blood. High levels of
• GnRH are associated most strongly with high levels of J-LH about 10 minutes later as these variables were measured in these investigations.
• episode length, episode criterion, and persistence can tend to blur results for delay.
• these additional analysis parameters do tend to account for more systematic variation in the data as indicated by more extreme positive and negative values.
• Tables 2 through 5 and Figures 2 and 3 portray interactions involving only five hormone variables, these examples illustrate how this invention could be used to explore, discover, and confirm thousands or millions of interactions involving hundreds or thousands of products of gene expression including proteins. Many of the computational procedures could be automated. As such, this invention can be an important and valuable contribution to bioinformatics. Furthermore, this invention can be applied to objects of investigation that are of other types as illustrated with Table 6.
• Table 6 is an ACP that portrays internal control of the United States economy.
• the action involves ten variables used to obtain values of the Index of Leading Economic Indicators, one variable that is a measure of the Gross Domestic Product of the United States economy, and one variable that is the Index of Leading Economic Indicators.
• the data were obtained from The Conference Board and are for 169 consecutive quarters ending recently.
• the scoring protocol for Table 6 involves using residuals from second order polynomial regression of each variable on measurement occasion and 12 dichotomous series to represent levels of each variable.
• Table 6 is for delay equals 2. Default values were used for episode length, episode criterion, and persistence. Table 6 portrays summary strength of longitudinal association scores.
• MEMS MicroElectroMechanical Systems

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