JP6198161B2 - Dynamic network biomarker detection apparatus, detection method, and detection program - Google Patents

Description

  The present invention relates to a detection apparatus, a detection method, and a detection program for detecting a candidate for a biomarker that is an index of a symptom of a living body based on measurement data of a plurality of factor items obtained by measurement related to the living body.

  According to various research results, the process of aggravation of many diseases, especially complex diseases, reaches a so-called bifurcation point when a certain critical threshold is exceeded, as in systems such as the climate system, ecological system, and economic system. Then, a state transition suddenly occurs, and the state changes suddenly from a healthy state to a disease state (see, for example, Non-Patent Documents 1 to 5). In research on the dynamic mechanism of such complex diseases, the progression process of disease deterioration (eg, asthma attack, cancer onset) is modeled as a time-dependent nonlinear dynamic system, and the modeled system is observed It has already been clarified that the disease rapidly deteriorates due to the phase transition at the branch point (see Non-Patent Documents 1 and 6).

  FIG. 1 is an explanatory diagram conceptually showing a disease progression process. FIG. 1a schematically illustrates the disease progression process. In FIG. 1, b, c, and d show the stability of the modeled system described above as a potential function in the course of the progress process, the horizontal axis indicates the time of progress, and the vertical axis indicates the value of the potential function. Is a graph conceptually shown. As shown in FIG. 1a, the progression process of disease deterioration can be expressed as a normal state (health state), a pre-disease state, and a disease state. In the normal state, the system is stable and the value of the potential function is the minimum value as shown by the position of the black circle in FIG. In the pre-disease state, the system has a high potential function value, as shown by the black circle position in FIG. Therefore, it is in a state that is easily affected by disturbance, and is located near the branch point where the phase transition occurs only by receiving a small disturbance, that is, at the limit of the normal state. However, the pre-disease state can be easily restored to a normal state by appropriate treatment. On the other hand, in a disease state, the system is stable, and the value of the potential function is a global minimum as shown by the black circle position in FIG. Therefore, it is difficult for this disease state caused by the phase transition from the normal state to recover to the normal state. (In the figure, for the sake of convenience, it is indicated as unrecoverable)

  Therefore, if a pre-disease state is detected and the patient can be notified of the transition to the disease state prior to the transition to the disease state, the patient can be removed from the pre-disease state by taking appropriate measures. There is a high possibility that it can be restored to a normal state.

  That is, if a branch point (critical threshold) can be detected, critical transition can be predicted, and early diagnosis of a disease can be realized.

  Conventionally, biomarkers have been used as methods for diagnosing disease states. Conventional biomarkers that have been used in the past include body fluids such as serum and urine collected from living bodies, as well as molecular level DNA, RNA, proteins, metabolites, etc. contained in tissues. It is an index that can quantitatively grasp the scientific change. The conventional method for diagnosing diseases using biomarkers is to compare the biomarkers extracted from normal samples (samples collected in a healthy state) with the biomarkers extracted from abnormal samples (samples collected in a disease state). It is a method of diagnosing.

Venegas, JG et al., “Self-organized patchiness in asthma as a prelude to catastrophic shifts.” (UK), Nature , Nature Publishing Group, 2005, 434, 777-782. McSharry, PE, Smith, LA, Tarassenko, L, “Predicting epileptic seizures: is a non-linear method appropriate? (Prediction of epileptic seizures: are nonlinear methods relevant.) "(UK), Nature Medicine, Nature Publishing Group, 2003, 9, p. 241-242 Roberto, PB, Eliseo, G., Josef, C., “Transition models for change point estimation of logistics regression (Transition models for change -point estimation in logistic regression.), (USA), Statistics in Medicine, Wiley-Blackwell, 2003, Vol. 22, p. 1141-1116 Paek, S. et al., “Hearing preservation after gamma knife stereotactic radiosurgery of vestibular schwannoma”, (USA), Cancer, Wiley Black Wiley-Blackwell, 2005, 1040, p. 580-590 Liu, JK, Rovit, RL, Cudwell, WT, “Pituitary Apoplexy”, (USA), Seminars Seminars in neurosurgery, Thieme 2001, 12, p. 315-320 Tanaka, G., Tsumoto, K., Tsuji, S., Aihara, K., "Intermittent hormone therapy for prostate cancer (Bifurcation analysis on a hybrid systems model of prostate cancer for prostate cancer.), (USA), Physical Review, American Physical Society, 2008, Vol. 237, p. . 2616-2627

  However, in the case of complex diseases, it is very difficult to predict critical transitions. The reason is as follows.

  First, the pre-disease state is the boundary of the normal state, and it is difficult for significant changes to occur before reaching the bifurcation point. Therefore, it is difficult to distinguish between the normal state and the pre-disease state in the diagnosis by the conventional technique such as biomarker and snapshot measurement.

  Secondly, among various studies, a reliable disease model capable of accurately detecting a warning signal for early diagnosis for predicting a branch point has not been developed yet. In particular, even in the same disease, since the progression process of disease deterioration varies from individual to individual, the model-based diagnosis method has a low probability of success.

  Third, the target of the pre-disease state is the patient, and usually the number of samples obtained from a single patient is limited, making it difficult to obtain sufficient samples for prediction over a long period of time. It is.

  In addition, a conventional method for diagnosing a disease using a biomarker is a method of making a diagnosis by comparing a normal state and a disease state, and at the time of diagnosis, the patient has already entered a disease state and has returned to the previous normal state. It is difficult to go back.

  In contrast, the present invention provides a detection device, a detection method, and a detection program that can detect a pre-disease state before transitioning to a disease state, and further, a disease model is unnecessary and diagnosis is performed with only a few biological samples. An object of the present invention is to provide a detection device or the like that can assist.

In order to solve the above-described problem, the detection apparatus according to the present invention selects a biomarker candidate serving as an index of a symptom of a living body to be measured based on measurement data of a plurality of factor items obtained by measurement related to a living body. A detection device for detecting, comprising: a detecting means for detecting a candidate for the biomarker based on a correlation between a time series change in measurement data of the factor item and a time series change in measurement data between the factor items. It is characterized by.

Further, in the detection apparatus according to the present invention, it further comprises a differential test means for testing whether or not the measurement data of the factor item changes with time, the factor item in the detection means is the It is a factor item that has been tested to be significant in the change over time by the differential test means.

In the detection apparatus according to the present invention, the differential test means performs a significance test based on a comparison result between the measurement data of the factor items and preset reference data.

In the detection apparatus according to the present invention, the factor item includes at least one of a measurement item relating to a gene, a measurement item relating to a protein, a measurement item relating to a metabolite, and a measurement item relating to an image obtained from a living body. And

Further, in the detection device according to the present invention, based on the biomarker candidates detected by the detection means, the presence or absence of an abnormality related to the living body, the state of the pre-disease state before the transition from the normal state to the disease state of the living body is detected. The information processing apparatus includes a unit that outputs information for supporting determination regarding at least one of the determinations regarding whether or not the disease is likely to develop in the living body.

The detection device having the above-described characteristics can detect a biomarker candidate serving as a warning signal indicating a pre-disease state before transitioning from a normal state to a disease state. Moreover, if the biomarker can be specified, the pre-disease state can be determined by collecting only a few samples from the detection target.

Furthermore, the detection method according to the present invention uses a detection device that detects biomarker candidates that are indicators of symptoms of a living body that is a measurement target, based on measurement data of a plurality of factor items obtained by measurement related to the living body. A detection method for detecting a candidate for the biomarker based on a correlation between a time series change in measurement data of the factor item and a time series change in measurement data between the factor items. It is characterized by.

Further, in the detection method according to the present invention, a differential test step is performed to test whether the measurement data of the factor item changes with time, and the factor item in the detection step It is characterized by being a factor item that has been tested to be significant in change over time by a differential test step.

Further, in the detection method according to the present invention, the differential test step performs a test of significance based on a comparison result between the measurement data of the factor item and preset reference data. .

In the detection method according to the present invention, the factor item includes at least one of a measurement item relating to a gene, a measurement item relating to a protein, a measurement item relating to a metabolite, and a measurement item relating to an image obtained from a living body. And

Further, in the detection method according to the present invention, based on the biomarker candidates detected in the detection step, the presence or absence of an abnormality relating to the living body, the pre-disease state before the transition from the normal state to the disease state for the living body And a step of outputting information for assisting determination regarding at least one of the possibility of the occurrence of the disease and the possibility that the living body develops.

The detection method having the above characteristics can detect a biomarker candidate serving as a warning signal indicating a pre-disease state before a transition from a normal state to a disease state. Moreover, if the biomarker can be specified, the pre-disease state can be determined by collecting only a few samples from the detection target.

Furthermore, the detection program according to the present invention is a process for detecting a biomarker candidate serving as an index of a symptom of a living body to be measured based on measurement data of a plurality of factor items obtained by measurement related to a living body on a computer. A detection program for detecting a candidate for the biomarker based on a correlation between a time series change in measurement data of the factor item and a time series change in measurement data between the factor items A step is executed.

Further, in the detection program according to the present invention, the computer is caused to execute a differential test step for testing whether or not the measurement data of the factor item has changed with time, and the factor item in the detection step Is a factor item that has been tested to be significant over time by the differential test step.

In the detection program according to the present invention, as the differential test step, a significance test is executed based on a comparison result between the measurement data of the factor item and preset reference data. .

In the detection program according to the present invention, the factor item includes at least one of a measurement item relating to a gene, a measurement item relating to a protein, a measurement item relating to a metabolite, and a measurement item relating to an image obtained from a living body. And

Further, in the detection program according to the present invention, based on the biomarker candidates detected in the detection step, the presence or absence of an abnormality relating to the living body, the pre-disease state before the transition from the normal state to the disease state for the living body And a step of outputting information for supporting determination on at least one of diseases that are likely to develop and the possibility of developing the living body.

  When the detection program having the above features is executed by a computer, the computer operates as a detection device according to the present invention. Therefore, it is possible to detect a biomarker candidate serving as a warning signal indicating a pre-disease state before transitioning from a normal state to a disease state. Moreover, if the biomarker can be specified, the pre-disease state can be determined by collecting only a few samples from the detection target.

  According to the present invention, a diagnosis is performed by collecting a biological sample from a diagnosis target and examining whether the biomarker serving as a warning signal indicating a pre-disease state immediately before a disease state exists in the collected biological sample. Whether or not the subject is in a pre-disease state can be diagnosed. Therefore, it is not necessary to construct a disease deterioration model, and it is not necessary to specify a driving factor for disease deterioration, and early diagnosis of the disease can be realized at a stage before becoming a disease.

FIG. 1 is a schematic diagram illustrating a disease progression process. FIG. 2 is a schematic view illustrating the dynamic characteristics of DNB according to the detection method of the present invention. FIG. 3 is a flowchart illustrating an example of the DNB detection method according to the embodiment. FIG. 4 is a flowchart illustrating an example of differential biomolecule selection processing according to the embodiment. FIG. 5 is a flowchart illustrating an example of DNB candidate selection processing according to the embodiment. FIG. 6 is a flowchart illustrating an example of DNB determination processing according to the embodiment. FIG. 7 is a diagram illustrating an example of a diagnosis schedule according to a disease early diagnosis method using DNB according to the embodiment. FIG. 8 is a flowchart illustrating an example of a disease early diagnosis method using DNB in the embodiment. FIG. 9 is an example of a graphic displaying a disease risk proportional to the overall index I. FIG. 10 is an example of a graphic displaying a disease risk proportional to the overall index I. FIG. 11 is a block diagram illustrating a configuration example of a detection device according to the present invention. FIG. 12 is a flowchart showing an example of DNB detection processing by the detection apparatus according to the present invention. FIG. 13 is a table showing diagnostic data in the first verification example. FIG. 14A is a graph illustrating an example of a time-series change in the average value of the standard deviations of the detected DNB candidates in the first verification example. FIG. 14B is a graph illustrating an example of a time-series change in the average value of the absolute values of the Pearson correlation coefficients between members of the detected candidate cluster of the DNB in the first verification example. FIG. 14C is a graph showing an example of a time-series change in the average value of the absolute values of the Pearson correlation coefficients between members of the detected DNB candidate cluster and other genes in the first verification example. . FIG. 14D is a graph illustrating an example of a time-series change in the average value of the overall indexes of the detected DNB candidates in the first verification example. FIG. 15 is a map showing an example of dynamic characteristics of DNB over time in a network configured by case group genes in the first verification example. FIG. 16 is a table of diagnostic data in the second verification example. FIG. 17A is a graph illustrating an example of a time-series change in an average value of standard deviations of detected DNB candidates in the second verification example. FIG. 17B is a graph illustrating an example of a time-series change in the average value of the absolute values of the Pearson correlation coefficients between the members of the detected candidate cluster of the DNB in the second verification example. FIG. 17C is a graph showing an example of a time-series change in the average absolute value of the Pearson correlation coefficient between a member of a detected candidate cluster of DNB and another gene in the second verification example. . FIG. 17D is a graph illustrating an example of a time-series change in the average value of the total indexes of the detected DNB candidates in the second verification example.

  The inventors of the present invention utilize genomic high-throughput technology capable of obtaining thousands of information, that is, high-dimensional data, from one sample, and based on the bifurcation process theory, A mathematical model was constructed to study the progression mechanism of disease deterioration at the molecular network level. As a result, the existence of a dynamic network biomarker (DNB) capable of detecting the immediately preceding branch (abrupt deterioration) state before the occurrence of the critical transition in the pre-disease state was elucidated. If the dynamic network biomarker is used as a warning signal for a pre-disease state, a disease model is unnecessary, and early diagnosis of complex diseases can be realized with only a few samples. Below, the form for implementing this invention based on a dynamic network biomarker is demonstrated.

<Theoretical basis>
First, the theoretical basis on which the present invention is based will be described. In general, a system related to a disease progression process (hereinafter referred to as system (1)) can be expressed by the following equation (1).

    Z (k + 1) = f (Z (k); P) Formula (1)

Here, Z (k) = (z1 (k),..., Zn (k)) is the dynamic state of the system (1) observed at time k (k = 0, 1,...). It can be used as information such as gene expression level, protein expression level, metabolite expression level. Specifically, it is information such as the concentration and the number related to molecules such as genes and proteins. P is a slowly changing parameter that drives the state transition of the system (1), and may be information on genetic factors such as SBP and CNV, and non-genetic factors such as methylation and acetylation, for example. it can. f = (f1,..., fn) is a nonlinear function of Z (k).

  Each of the normal state and the disease state can be expressed by an attractor of the state equation Z (k + 1) = f (Z (k); P). Since the progression process of complex diseases has very complex dynamic properties, the function f is a nonlinear function with thousands of variables. Moreover, the element P that drives the system (1) is difficult to specify. For this reason, it is very difficult to construct and analyze a system model of a normal state and a disease state.

  On the other hand, the inventors of the present invention focused on the critical transition state of the system immediately before the transition from the normal state to the disease state, that is, the pre-disease state. Generally, the system (1) has an equilibrium point having the following characteristics.

1. If Z ^* is a fixed point of the system (1), Z ^* = f (Z ^* ; P),
2. If Pc is a threshold at which the system branches, when P = Pc, one real eigenvalue of Jacobian matrix ∂f (Z; Pc) / ∂Z | Z = Z ^* or an absolute value of a pair of complex conjugate eigenvalues Becomes 1,
3. When P ≠ Pc, the absolute value of the eigenvalue of the system (1) is generally not 1.

  The inventors theoretically clarified from the above characteristics that when the system (1) enters a critical transition state, the following unique characteristics appear. That is, when the system (1) is in a critical transition state, in the network (1) in which each variable z1,..., Zn of the system (1) is configured as a node, a control group (sub Network) appears. The dominant group appearing in the critical transition state ideally has the following characteristics.

(I) When zi and zj are nodes belonging to the dominating group,
PCC (zi, zj) → ± 1;
SD (zi) → ∞;
SD (zj) → ∞.
(II) If zi is a node belonging to the dominating group, but zj is not a node belonging to the dominating group,
PCC (zi, zj) → 0;
SD (zi) → ∞;
SD (zj) → boundary value.
(III) When zi and zj are not nodes belonging to the dominating group PCC (zi, zj) → α, αε (−1,1) \ {0};
SD (zi) → boundary value;
SD (zj) → boundary value.

  Here, PCC (zi, zj) is a Pearson correlation coefficient between zi and zj, and SD (zi) and SD (zj) are standard deviations between zi and zj.

That is, in the network (1), the appearance of a dominant group having the above-mentioned unique characteristics (I) to (III) is taken as an indication that the system (1) is transitioning to a critical transition state (pre-disease state). be able to. Therefore, the critical transition of the system (1) can be detected by detecting the dominating group. That is, the control group can be a warning signal indicating a critical transition, that is, a pre-disease state immediately before a disease worsening. Then, no matter how complicated the system (1) is, even if the driving element is unknown, if only the dominant group that becomes the warning signal is detected, the mathematical model of the system (1) is not handled directly, It is possible to identify the pre-disease state. By specifying the pre-disease state, it is possible to realize advance measures and early treatment for the disease.

  In the present invention, the dominant group serving as a warning signal indicating a pre-disease state is referred to as a dynamic network biomarker (hereinafter abbreviated as DNB).

<DNB characteristics and criteria>
As described above, the DNB is a dominating group having unique characteristics (I) to (III), and as a sub-network composed of a plurality of nodes, when the system (1) enters a pre-disease state, Appears in 1). In the network (1), if each node (z1,..., Zn) is a factor item to be measured for a biomolecule such as a gene, protein, metabolite, etc., the DNB has the unique characteristic (I ) To (III) are groups (subnetworks) composed of factor items related to some biomolecules.

  Furthermore, the conditions for determining DNB can be determined as follows based on the specific characteristics (I) to (III).

Condition (I): a group consisting of some biomolecules (genes, proteins or metabolites) existing in the network (1), and the absolute value of the Pearson correlation coefficient PCC between the biomolecules in the group The average value of increases significantly.
Condition (II): The average absolute value of the Pearson correlation coefficient OPCC between the biomolecules in the group and other biomolecules is significantly reduced.
Condition (III): The average value of the standard deviation SD of the biomolecules in the group is significantly increased.

  A group consisting of biomolecules that simultaneously satisfy the above conditions (I) to (III) is recognized as DNB.

  In order to intuitively describe the characteristics of the DNB, the dynamic characteristics of the DNB in the network will be described below by taking a network having six nodes as an example. FIG. 2 is a schematic view illustrating the dynamic characteristics of DNB according to the detection method of the present invention. FIG. 2a shows a normal state, a pre-disease state, and a disease state as disease progression processes. B, c and d in FIG. 2 show the stability of the modeled system (1) as a potential function in the normal state, the pre-disease state and the disease state with respect to the progress process, and the horizontal axis shows the course. It is the graph which took time and took the value of the potential function on the vertical axis, and showed conceptually. Moreover, e, f, and g of FIG. 2 have shown notionally the example of the network state of the system (1) corresponding to each of a normal state, a pre-disease state, and a disease state. Further, h in FIG. 2 shows an example of the temporal change in the molecular concentration that is a factor item of DNB in the pre-disease state.

  Nodes z1 to z6 represent factor items related to different types of biomolecules such as genes, proteins, and metabolites, and a connection line between the nodes z1 to z6 indicates a correlation between the nodes, and The size indicates the size of the Pearson correlation coefficient PCC, and the drawing method of circles surrounding z1 to z6 indicates the size of the standard deviation SD of each node. As a drawing method of the connection line, the standard deviation SD is the smallest when the circle is blank, and the standard deviation SD is larger as the slanted line in one direction and the slanted line in two directions.

In a normal state, as shown in FIG. 2e, each node is at an intermediate level where the mutual correlation and the respective standard deviations are all equal. However, when it comes to pre-illness,
Groups (z1 to z3) that show remarkably unique characteristics appear compared to other nodes. The nodes z1 to z3 in the group have a significantly increased Pearson correlation coefficient between the nodes z1 to z6 and a significant decrease in the Pearson correlation coefficient with the other nodes z4 to z6, as shown in f of FIG. Yes. Further, the standard deviation of the nodes z1 to z3 in the group is large. The reason is that the nodes z1 to z3 in the group have drastic changes in density at different times (t = 1, t = 2, t = 3) as shown in h of FIG.

  However, after transition to the disease state, as shown in FIG. 2g, the standard deviation of each node z1 to z3 in the group is slightly larger, but the Pearson correlation coefficient between the nodes is equal. Returning to the middle level. That is, the unique characteristics of the group (z1 to z3) are lost.

  As conceptually shown in FIG. 2, in the pre-illness state, a dominant group including some nodes having good characteristics appears. In this application, such a control group, referred to as DMB, is used as a biomarker for early diagnosis of disease, as a warning signal that indicates that it is in a pre-disease state and can predict the transition to the disease state. Can do. DNB is a subnetwork that appears in a network whose characteristics are different, unlike a static biomarker used for normal disease diagnosis. Therefore, in the present application, the dominant group is referred to as DNB (Dynamic Network Biomarker).

<Warning signal>
As described above, DNB can be used for early diagnosis of a disease as a warning signal indicating a pre-disease state. For measuring the intensity of the warning signal, the average value of the absolute value of the Pearson correlation coefficient PCC between the nodes in the DNB described above, and the absolute value of the Pearson correlation coefficient OPCC between the node in the DNB and another node are used. And the standard deviation SD of DNB can be used. Furthermore, it is possible to introduce a comprehensive index I that can comprehensively reflect the characteristics of DNB. In the present invention, as an example, an overall index I represented by the following formula (2) is introduced.

  I = SDd × PCCd / OPCCd (2)

  In Equation (2), PCCd represents the average value of the absolute value of the Pearson correlation function between the nodes in the DNB, and OPCCd represents the average value of the absolute value of the Pearson correlation coefficient between the node in the DNB and another node. , SDd represents an average value of standard deviations of nodes in the DNB. As can be seen from the equation (2), when the SDd and the PCCd increase and the OPCCd decreases, the overall index I greatly increases. Therefore, the characteristics of DNB can be detected with high sensitivity. Further, the distance from the disease state can be grasped to some extent from the value of the comprehensive index I.

<DNB detection method>
FIG. 3 is a flowchart illustrating an example of the DNB detection method according to the embodiment. In the detection method of the present invention, first, it is necessary to obtain measurement data by measurement related to a living body. If a high-throughput technique such as a DNA chip is used, 20,000 or more genes can be measured from one biological sample. In order to perform statistical analysis, in the present invention, a plurality (6 or more) of biological samples are collected at different time points from the measurement target, and the measurement data obtained by measuring the collected biological samples are aggregated and statistically collected. Get the data. The DNB detection method according to the present invention mainly includes a high-throughput data acquisition process (s1), a differential biomolecule selection process (s2), a clustering process (s3), as shown in FIG. , DNB candidate selection processing (s4), and DNB determination processing (s5) by significance analysis. Next, each of these processes will be described in detail.

In the high-throughput data acquisition process in step s1, the sample to be detected is a case sample, the reference sample is a control sample, and the high-throughput physiological data, that is, the expression level of a biomolecule is measured from each sample. This is a process of acquiring data (for example, microarray data). The reference sample is a sample such as a sample collected in advance from a patient to be examined or a sample collected first at the time of collection, and is used as a control sample having a purpose such as calibration of a measuring apparatus. A control sample is not necessary, but is useful for eliminating error factors and improving measurement reliability.

  The selection process of the differential biomolecule in step s2 is a process of selecting a biomolecule that shows a significant change in the expression level. FIG. 4 is a flowchart illustrating an example of differential biomolecule selection processing according to the embodiment. FIG. 4 shows in detail the biological treatment of the differential biomolecule in step s2 shown in FIG.

  As shown in FIG. 4, first, statistical data based on high throughput data (biomolecule expression level) measured from each of n case samples is set as D1c, and data measured from the control sample is set as Dr (s21). Next, Student's t-test is performed on each case sample biomolecule D1c, and a biomolecule D2c that shows a significant change in expression level compared to the high-throughput data Dr of the control sample is selected (s22). In step s22, Student's t-test is exemplified as a method for selecting the biomolecule D2c showing a significant change in the expression level, but the method is not particularly limited. For example, other test methods such as Mann-Whitney U test can be applied, and such a non-parametric test is particularly effective when the population D1c does not follow a normal distribution. is there. Also, in the case of Student's t-test, the value of the significance level α can be appropriately set to a value such as 0.05 or 0.01.

  Next, using the false expression rate FDR, for each case sample biomolecule D2c, multiple comparisons or multiple student t-tests are corrected, and each case sample gene or protein data D3c after correction is corrected. Is selected (s23). Next, using the two-fold change method, Dc whose standard deviation SD changes relatively remarkably is selected as a differential biomolecule from each corrected case sample gene or protein data D3c. . The differential biomolecule Dc selected here not only shows a significant difference compared to the biomolecule Dr of the control sample, but also deviates greatly from its own average value. Also in step s23, the test method is not limited to the student t test.

  Next, clustering processing (s3 in FIG. 3) is performed. The clustering process here is a process of classifying a plurality of biomolecules into groups highly correlated with each other, and each group into which biomolecules are classified is referred to as a cluster. That is, the differential biomolecule Dc selected in step s24 shown in FIG. 4 is classified into n clusters so that biomolecules highly correlated with each other are made into one cluster. All the resulting clusters are potential dominating groups, ie, candidates for DNB to be detected.

Next, a DNB candidate selection process (s4) shown in FIG. 3 is performed. FIG. 5 is a flowchart illustrating an example of DNB candidate selection processing according to the embodiment. FIG. 5 shows details of the DNB candidate selection process in step s4 shown in FIG. That is, the DNB candidate selection process is performed based on the flowchart of the DNB candidate selection process shown in FIG. In the cyclic loop shown in FIG. 5, for all clusters (k) (k = 1,..., N), the average absolute value PCCd (k) of the Pearson correlation coefficient between the nodes in the cluster, the cluster Calculate the mean value OPCCd (k) of the absolute value of the Pearson correlation function between the nodes in the cluster and other nodes, the mean value SDd (k) of the standard deviation of the nodes in the cluster, and the overall index I (k) (S41-s46). Then, the cluster having the largest overall index I value is selected from all the clusters as a DNB candidate (s47).

  Next, DNB determination processing (s5 in FIG. 3) is performed by significance analysis shown in FIG. FIG. 6 is a flowchart illustrating an example of DNB determination processing according to the embodiment. FIG. 6 shows details of the DNB determination processing by the superiority analysis in step s5 shown in FIG. That is, based on the above-described DNB determination conditions (I) to (III), it is determined whether or not the cluster (m) selected as a DNB candidate in step s47 is a DNB. Although it can be determined by various significance analyses, as an example, the processing is performed based on the flowchart of the DNB determination processing shown in FIG.

  As shown in FIG. 6, first, the average value PCCdr of the absolute value of the Pearson correlation coefficient between the nodes of the data acquired from the control sample and the average value SDdr of the standard deviation of each node are calculated (s51, s52). ). Then, compared with the average value PCCdr of the Pearson correlation coefficient of the control sample, the average value PCCd (m) of the absolute value of the Pearson correlation coefficient between the nodes in the cluster (m) selected in step s47 is significantly increased. It is determined whether or not (s53). If it is determined that there is no significant increase (NO), a result that there is no DNB is output, and the process is terminated (s57). On the other hand, if it is determined that it has increased significantly (YES), the process proceeds to the next step s54. In step s54, whether the average value OPCCd (m) of the Pearson correlation coefficient between each node in the cluster (m) and another node is significantly reduced as compared to the average value PCCdr of the Pearson correlation coefficient of the control sample It is determined whether or not (s54). If it is determined that there is no significant reduction (NO), a result indicating that there is no DNB is output (s57), and the process is terminated. On the other hand, if it is determined that it has been significantly reduced (YES), the process proceeds to the next step s55. In step s55, it is determined whether or not the standard deviation average value SDd (m) of the nodes in the cluster (m) has significantly increased compared to the standard deviation average value SDr of the control sample. If it is determined that there is no significant increase (NO), it is determined that there is no DNB (s57), and the process is terminated. On the other hand, if it is determined that it has increased significantly, the cluster (m) is recognized as DNB (s56), and the process is terminated.

<Early diagnosis method of disease by DNB>
As a diagnosis schedule, it is desirable to perform a plurality of diagnoses at regular intervals and to take several samples for each diagnosis. FIG. 7 is a diagram illustrating an example of a diagnosis schedule for early diagnosis of a disease using DNB in the embodiment. As shown in FIG. 7, samples are taken at a plurality of stages (stage 1, stage 2,..., Stage T). Regarding the number of samples collected at each stage, it is generally desirable to collect 6 or more samples in one stage in order to ensure data accuracy. The interval between two successive stages can be set as long as days, weeks, months or years depending on the disease situation, but each stage takes samples at different time points in a short period of time. It is desirable to do. For example, 6 samples are taken at 6 time points of the day. The interval between each time point can be, for example, several minutes or several hours depending on the situation.

  FIG. 8 is a flowchart illustrating an example of a disease early diagnosis method using DNB in the embodiment. As shown in FIG. 8, the disease early diagnosis method using DNB mainly includes a sample collection process (s100), a differential biomolecule selection (s200), a DNB candidate selection process (s300), A DNB determination process based on significance analysis (s400) and a diagnosis result output process (s500) are included. Next, these processing contents will be described in detail.

  Sample collection processing (s100): Similar to a general disease diagnosis method, a sample for collecting necessary physiological data is collected according to the disease to be diagnosed. For example, in the case of liver damage, samples such as blood and liver tissue are collected.

  In addition to using a sample collected from the diagnosis target when making a diagnosis as a case sample, as a reference sample, a sample collected from a healthy person other than the diagnosis target or the first sample collected from the diagnosis target is a control sample. It can be.

  Selection of differential biomolecule (s200): According to the differential biomolecule selection process flowchart shown in FIG. 4, a differential biomolecule is selected from the sample collected in step s100.

  DNB candidate selection process (s300): According to the DNB candidate selection flowchart shown in FIG. 5, a dominant group that is a candidate for DNB is selected from the differential biomolecules selected in step s200.

  DNB determination process by significance analysis (s400): According to the flowchart showing the DNB determination method by significance analysis shown in FIG. 6, it is determined whether or not the DNB candidate selected in step s300 is a DNB. .

  Diagnosis result output (s500): If it is determined in step s400 that there is no DNB, the DNB candidate data selected in step s300 is stored in the storage device as reference data for the next diagnosis, and an abnormality is recognized. Outputs the diagnostic result to the effect. On the other hand, if it is determined in step s400 that there is a cluster certified as a DNB, the biomolecule data of the certified cluster is stored as a member of the DNB, and a diagnosis result indicating that it is in a pre-disease state is output. In addition, a diagnosis result associated with the detected DNB can be output. Here, although shown as a diagnosis result, in detail, for example, it is a result used as a reference for diagnosis by a doctor, and the diagnosis result output in step s500 is not a diagnosis by the doctor itself but a doctor's diagnosis. This is output data that assists diagnosis and serves as a reference for diagnosis to support diagnosis.

  For example, a comprehensive index I that comprehensively reflects the characteristics of DNB can be output. The larger the overall index I, the closer to the branch point, the higher the warning effect when the disease risk proportional to the overall index I is output in the form of a graphic or image that can be seen intuitively.

  FIG. 9 and FIG. 10 are examples of figures that display disease risk proportional to the overall index I. FIG. In FIG. 9, the entire arrow indicates the pre-disease state (pre-morbidity), the flow in the direction indicated by the arrow indicates the temporal change of the disease state (onset), and the diamond mark located on the left side of the arrow indicates diagnosis. It is a disease risk pointer whose position changes according to the value of the obtained overall index I. The larger the value of the overall index I is, the larger the diamond mark is set to approach the right side of the arrow.

  In addition, when an early diagnosis of a disease has been performed using DNB before, a disease risk proportional to the overall index I is displayed together with the overall index obtained in the previous diagnosis as shown in FIG. be able to. In FIG. 10, the dotted rhombus marks indicate the total index obtained by the diagnosis on July 1, 2011, and the solid rhombus marks indicate the total index obtained by the diagnosis on September 1, 2011. It is possible to intuitively determine that the disease state is approached from the positional change of the rhombus mark.

  Further, as information associated with the DNB, a map of all networks including the detected DNB or a part of the network including the DNB (for example, FIG. 15 described later) can be output.

Furthermore, a list of biomolecules that are members of DNB can be output. As described above, DNB appears in a pre-disease state that transitions from a normal state to a disease state, but a biomolecule detected as DNB, that is, a gene, protein, or metabolite itself is not necessarily a factor that exacerbates the disease. It is not necessarily a pathological gene, protein, or metabolite. However, some DNB members have been found to be associated with disease.

  Therefore, if a substance (gene, protein or metabolite) related to a specific disease contained in the detected member of DNB is extracted, for example, a diagnosis subject such as a examiner may develop due to a doctor's diagnosis. A certain degree of sexual disease can be grasped.

  Therefore, following the above-mentioned “output of diagnostic results” (s500 in FIG. 8), a database storing the correspondence between genes, proteins or metabolites and diseases is used to detect the relationship between the detected DNB and the disease. A gene, protein, or metabolite can be extracted and output as a diagnostic result that serves as a reference for diagnosis.

  Then, for example, when blood is collected from a person undergoing a health check and DNB is detected from the gene, protein or metabolite data obtained from the blood, the gene, protein or metabolite contained in the DNB is detected. Can be identified to some extent, and potential illnesses of the examiner can be diagnosed at an early stage.

<Detection device>
The DNB detection method detailed above can embody the present invention as a detection device using a computer. FIG. 11 is a block diagram illustrating a configuration example of a detection device according to the present invention. The detection apparatus 1 shown in FIG. 11 is realized using a personal computer, a client computer connected to a server computer, and other various computers. The detection apparatus 1 includes various mechanisms such as a control unit 10, a recording unit 11, a storage unit 12, an input unit 13, an output unit 14, and an acquisition unit 15.

  The control unit 10 is configured using a circuit such as a CPU (Central Processing Unit), and is a mechanism that controls the entire detection apparatus 1.

The recording unit 11 is a non-volatile auxiliary recording mechanism such as a magnetic recording mechanism such as an HDD (Hard Disk Drive) or a non-volatile semiconductor recording mechanism such as an SSD (Solid State Disk). The recording unit 11 records various programs and data such as the detection program 11a according to the present invention.

  The storage unit 12 is a volatile main storage mechanism such as an SDRAM (Synchronous Dynamic Random Access Memory) or an SRAM (Static Random Access Memory).

  The input unit 13 is an input mechanism including hardware such as a keyboard and a mouse, and software such as a driver.

  The output unit 14 is an output mechanism including hardware such as a monitor and a printer, and software such as a driver.

  The acquisition unit 15 is a mechanism for acquiring various data from the outside. Specifically, these are various hardware such as a LAN (Local Area Network) port for capturing data via a communication network, a port connected to a dedicated line such as a parallel cable connectable to a measuring device, and software such as a driver. .

Then, by storing the detection program 11a recorded in the recording unit 11 in the storage unit 12 and executing it based on the control of the control unit 10, the computer can detect the detection program 11a.
The various procedures which concern on are performed, and it functions as the detection apparatus 1 of this invention. In addition, although it has divided into the recording part 11 and the memory | storage part 12 for convenience, both have the same function of recording various information, and which mechanism is made to record according to a specification, an operation form, etc. of an apparatus? Can be determined as appropriate.

  FIG. 12 is a flowchart showing an example of DNB detection processing by the detection apparatus 1 according to the present invention. The process of the detection apparatus 1 according to the present invention executes the above-described DNB detection process. The control unit 10 of the detection apparatus 1 uses the acquisition unit 15 to acquire measurement data for a plurality of factor items obtained by measurement related to the living body (Sc1). Step Sc1 corresponds to the high-throughput data acquisition process shown as step s1 in FIG. Here, since it is expressed as an object of computer processing, it is expressed as a factor item, but here the factor item is related to a measurement item related to a gene that can be a node of the above-mentioned DNB, a measurement item related to a protein, and a metabolite Measurement items such as measurement items are shown. It is also possible to use measurement items related to images obtained from in-vivo images output by a measurement device such as a CT scan.

  The control unit 10 tests whether or not each of the acquired measurement data of the factor items changes with time, and selects a differential biomolecule based on the test result (Sc2). Step Sc2 corresponds to the differential biomolecule selection process shown as step s2 in FIG.

  Therefore, in the process of step Sc2, the control unit 10 performs a test on significance based on the measurement data of each factor item and the comparison result with the factor item and reference data set in advance for each time series ( Sc21) includes a process (Sc22) for selecting a factor item that has been tested to be significant over time. That is, the various processes shown in FIG. 4 are executed. The data processed as reference data by the detection device 1 is a control sample. For example, the detection device 1 uses the reference data for the sample based on the setting such that the first acquired sample is a control sample. As the handling.

  The control unit 10 classifies the plurality of factor items into a plurality of clusters based on the correlation of the time series changes of the respective measurement data of the selected factor items (Sc3). Step Sc3 corresponds to the clustering process shown as step s3 in FIG.

  The control unit 10 corresponds to a selection condition set in advance from each classified cluster based on the correlation between the time series change of each measurement data of each factor item and the time series change of the measurement data between each factor item. A cluster is selected (Sc4). Step Sc4 corresponds to the DNB candidate selection process shown as step s4 in FIG.

Therefore, in the process of step Sc4, the control unit 10 calculates, for each cluster, the average value of the values indicating the correlation of the measurement data of each factor item in the cluster as the first index (Sc41). An average value of values indicating correlation between the measurement data of the factor item and the measurement data of the factor item outside the cluster is calculated as a second index (Sc42), and the standard deviation of the measurement data for each factor item in the cluster is calculated. The process (Sc43) which calculates an average value as a 3rd index | exponent is included. Further, in the process of step Sc4, the control unit 10 calculates a comprehensive index based on the product of the first index, the second index, and the reciprocal of the third index (Sc44), and the calculated total index is the maximum. A process of selecting a cluster (Sc45) is included. That is, various processes shown in FIG. 5 are executed. As the first index, the second index, the third index, and the overall index, for example, the average value PCCd (k) of the absolute value of the Pearson correlation coefficient between the nodes in the cluster, The average value OPPCd (k) of the absolute value of the Pearson correlation function between the nodes, the average value SDd (k) of the standard deviation of the nodes in the cluster, and the overall index I (k) are used.

  The control unit 10 detects the factor items included in the selected cluster as biomarker candidates (Sc5). Step Sc5 corresponds to the DNB determination process shown as step s5 in FIG.

  Therefore, in the process of step Sc5, the control unit 10 calculates the reference standard deviation indicating the average value of the standard deviation of the corresponding reference data for each factor item (Sc51), and averages the values indicating the correlation between the factor items. A reference correlation value indicating the value is calculated (Sc52). The first index increases with significance compared to the reference standard deviation, the second index decreases with significance compared to the reference correlation value, and the third index has significance compared to the reference standard deviation. When it increases, the process (Sc53) which detects the item contained in the said cluster as a biomarker is included. That is, various processes shown in FIG. 6 are executed. As the reference standard deviation and the reference correlation value, for example, the average value PCCdr of the absolute value of the Pearson correlation coefficient between the nodes and the average value SDdr of the standard deviation of each node are used.

  And the control part 10 outputs the factor item detected as a biomarker candidate from the output part 14 (Sc6), and complete | finishes a process.

<First verification example>
In order to verify the diagnostic accuracy of the method for early diagnosis of disease by DNB of the present invention, the diagnosis method of the present invention is used for diagnosis using the experimental data of the mouse in which the lung injury is caused, and the diagnosis result is used as the actual medical condition. Compared with the progress, the effectiveness of the diagnostic method of the present invention was verified. Next, the verification example will be described in detail. The experimental data is divided into a group of experiments and a control group of CD-1 male mice. The case group is in a normal air environment, and the control group is in an air environment containing phosgene, a toxic gas. It was obtained from an experiment to observe the health status of both groups of mice and investigate the molecular mechanisms of acute lung injury from phosgene inhalation. Using the experimental data, the health status of mice in case groups exposed to phosgene was diagnosed using the diagnostic method of the present invention. Normally, mice inhal a certain amount of phosgene and develop phosgene-induced lung injury.

FIG. 13 is a table showing diagnostic data in the first verification example. As shown in FIG. 13, the diagnosis object is a mouse (CD-1 male mouse) suffering from phosgene-induced lung injury, and the sample collection object is a mouse of a case group to be a diagnosis object and a control group to be a reference object. The sampling point is the time point when 0, 0.5, 1, 4, 8, 12, 24, 48, 72 hours have elapsed since the start of the experiment, and the sampling point of the gene used for the detection of DNB The number is 22690.

  Specifically, the following process was performed using the diagnostic method of the present invention.

  First, differentially expressed genes are selected from high-throughput gene data measured from each sample. At each sampling point, six case samples and six control samples are provided. At the first sampling point (0h), the case sample data is the same as the control sample data.

  At each sampling point, a differential gene of A = [0, 53, 184, 1325, 1327, 738, 980, 1263, 915], respectively, using Student's t-test with significance level p <0.05. Was elected.

Then, for each sampling point, B = [0, 29, 72, 195, respectively, by using false expression rate (FDR) and 2-fold modified screening for the selected set A of differential genes. 269, 163, 173, 188, 176] were selected.

  The selected gene set B was clustered so that highly correlated ones were collected into one cluster at each sampling point, and 40 clusters were obtained.

  Furthermore, at each sampling point, data normalization is performed for all the genes in the obtained 40 clusters. At each sampling point, the average value SDd (third index) of the standard deviation of each cluster in the normalized control group and case group, the average value of the absolute value of the Pearson correlation coefficient between the members of the cluster PCCd (second index), mean value OPCCd (first index) of absolute values of Pearson correlation coefficients between cluster members and other genes, and overall index I are calculated.

  At each sampling point, the cluster with the largest overall index I is selected as a DNB candidate from each cluster in the calculated case group, and the average value of the standard deviation of the control group is selected for the DNB candidate. Based on SDc and the average value PCCc of the absolute value of the Pearson correlation coefficient between genes, whether or not it is DNB is determined by significance analysis. As a result, the number of clusters serving as DNBs at each sampling point was [0, 0, 0, 0, 1, 0, 0, 0, 0].

  That is, DNB was detected at the fifth sampling point (8h), and the DNB is the 111th cluster having 220 genes.

  FIG. 14A is a graph illustrating an example of a time-series change in the average value SDd of standard deviations of detected DNB candidates in the first verification example. FIG. 14B is a graph illustrating an example of a time-series change in the average value PCCd of the absolute values of the Pearson correlation coefficients between members of the detected candidate cluster of the DNB in the first verification example. FIG. 14C is a graph showing an example of a time-series change in the average value OPCCd of the absolute value of the Pearson correlation coefficient between a member of the detected candidate cluster of DNB and another gene in the first verification example. is there. FIG. 14D is a graph showing an example of a time-series change of the overall index I of the detected DNB candidates in the first verification example. 14A to 14D, the horizontal axis represents the time stage t, and the vertical axis represents the average value SDd of the standard deviation (FIG. 14A) and the average value PCCd of the absolute value of the Pearson correlation coefficient between the members of the cluster, respectively. FIG. 14B) shows the average value OPCCd (FIG. 14C) of the absolute value of the Pearson correlation coefficient between cluster members and other genes, and the overall index I (FIG. 14D). The broken line shows the change with time of various indexes of DNB candidates detected from the case group, and the solid line shows the change with time of various indexes of one cluster selected from the control group.

As can be seen from FIGS. 14A to 14D, from the fourth time stage (ie, after 4 hours), the first index PCCd, the third index SDd, and the overall index I of the DNB candidates start to increase greatly, The peak is reached at the time stage (ie, after 8 hours). Meanwhile, the third candidate for DNB
The index OPCCd decreases from the second time stage and shows a local minimum at the same fifth time stage (ie, after 8 hours).

Moreover, in order to show the dynamic characteristic of DNB intuitively, the dynamic characteristic of the whole gene network containing DNB is shown in FIG. FIG. 15 is a map showing an example of dynamic characteristics of DNB over time in a network configured by case group genes in the first verification example. FIG. 15 shows the gene network of the case group (3452 genes, 9238 links) at each sampling point of 0.5, 1, 4, 8, 12, 24, 48, 72h in order. , A node indicated by “◯” is a gene belonging to a DNB candidate, a node indicated by “□” is another gene in the vicinity of the candidate node for DNB, and a node between nodes The line shows the correlation between both nodes. Further, the color density of “◯” indicates the magnitude of the standard deviation SD of the gene, and the density of the connection line of both nodes indicates the magnitude of the absolute value of the correlation coefficient PCC of both nodes. All maps are constructed using Cytoscape (http://www.cytoscape.org/).

As shown in FIG. 15, the DNB candidate characteristics (SD, PCC) change over time, and gradually evolve from a normal cluster that behaves the same as other genes to a DNB. In the fifth stage shown in FIG. 15e (when 8h elapses), the characteristic as the DNB is most remarkable, and the warning signal that is the pre-disease state (8h) is clearly shown. However, after transition to the disease state (24h, 48h, 72h), DNB members again behave the same as other genes.

The results show that the pre-disease state exists near the fifth time stage, and after the fifth time stage, the system transitions to the disease state.

  Therefore, when diagnosing with the DNB early diagnosis method according to the present invention, since the warning signal for illness is slightly visible in the diagnosis at the fourth time stage, a diagnosis result that the illness is getting worse is obtained soon. be able to. In the diagnosis at the fifth time stage, since the warning signal indicating the illness is clearly seen, a diagnosis result indicating that the illness will soon be obtained can be obtained.

  On the other hand, according to the results of actual mouse experiments, the case group of mice developed pulmonary edema 8 hours after inhalation of phosgene, 50% to 60% died after 12 hours, and another 60% after 24 hours. ~ 70% died.

  Therefore, it can be said that the diagnosis result of the early diagnosis by DNB of the present invention completely coincides with the actual disease deterioration situation of mice.

<Second verification example>
In the first verification example described above, the effectiveness of the method for early diagnosis of diseases by DNB of the present invention was verified using data from animal experiments. In this verification example, the diagnostic accuracy of the method for early diagnosis of disease by DNB of the present invention is further verified using clinical data of B cell lymphoma.

  FIG. 16 is a table listing diagnostic data in the second verification example. As shown in FIG. 16, based on clinical features, lesions and flow cytometry, the samples were resting (P1), active (P2), critical (P3), metastatic (P4), aggressive (P5 ) Is divided into groups of five stages. At each stage, the number of samples is 5, 3, 6, 5, 7 respectively. At each stage, the splenomegaly is “None”, “None”, “+/−”, “+”, “++++”, respectively. At each stage, the flow cytometry is “normal rest”, “normal activity”, “abnormal”, “mixed”, and “B-1 clone”, respectively. Moreover, let the sample extract | collected in the rest period (P1) be a control sample, and let the sample extract | collected in another stage (P2-P5) be a case sample.

From the gene expression data measured from the 26 samples, 13712 genes were targeted and diagnosed by the above-described early diagnosis method of disease by DNB. As a result, FIGS. 17A to 17D show indexes of DNB candidates detected from the genes of the case group. FIG. 17A is a graph showing an example of a time-series change in the average value SDd of the standard deviations of the detected DNB candidates in the second verification example. FIG. 17B is a graph illustrating an example of a time-series change in the average value PCCd of the absolute values of the Pearson correlation coefficients between members of the detected candidate cluster of the DNB in the second verification example. FIG. 17C is a graph showing an example of a time-series change in the average value OPCCd of the absolute value of the Pearson correlation coefficient between the member of the detected candidate cluster of DNB and another gene in the second verification example. is there. FIG. 17D is a graph illustrating an example of a time-series change in the overall index I of the detected DNB candidates in the second verification example.

  In FIG. 17A to FIG. 17D, the horizontal axis indicates the number of the stage (P1 to P4), and the vertical axis indicates the mean value SDd of the standard deviation (FIG. 17A) and the Pearson correlation coefficient between the cluster members, respectively. Average value PCCd of absolute values (FIG. 17B), average value OPCCd of absolute values of Pearson correlation coefficients between cluster members and other genes (FIG. 17C), and overall index I are shown (FIG. 17D). .

  As is apparent from FIGS. 17A to 17D, in the second stage (P2), that is, the active period, the overall index I of the DNB candidate has reached its peak, and the warning signal indicating the disease state is the strongest. . This diagnostic result is in complete agreement with the actual lesion. Even in actual clinical data, the condition worsens from the critical period immediately after the active period, splenomegaly becomes “+/−”, and flow cytometry becomes “abnormal”. Therefore, the DNB specific analysis result in the present verification example completely matches the actual clinical data.

  Further, in the normal diagnosis, as shown in FIG. 16, since the splenomegaly in the active period is “None” and the flow cytometry is “normal activity”, the diagnosis result is that no abnormality is observed. On the other hand, when diagnosing with the DNB disease early diagnosis method of the present invention, a warning signal (DNB) indicating a pre-disease state was detected in the active period, so that a diagnosis result that “a sign of abnormality is seen” is obtained. Can tell the patient. Therefore, the patient stops the worsening of the medical condition by taking treatment measures at an early stage.

  Therefore, it has been verified that the early diagnosis of diseases by DNB of the present invention is very effective for the early diagnosis of complex diseases such as lymphoma.

  In addition, in the DNB detected in this verification example, 22 genes and TFs are included, of which 13 genes are clearly associated with B-cell lymphoma. Eight of the genes have been found to be master regulators of growth. Therefore, the DNB according to the present invention can not only notify a patient of an abnormality at an early stage as a warning signal indicating a pre-disease state, but also can specifically identify a gene associated with the disease. It seems to be very useful for the treatment of complex diseases and pharmaceuticals.

  The above embodiment discloses only a part of the myriad examples of the present invention, and the design can be appropriately changed in consideration of various factors such as the type of disease and the purpose to be detected. is there. In particular, as the factor item, various measurement data can be used as long as it is information obtained by measurement related to a living body. For example, the measurement data is not limited to the measurement data related to the genes, proteins, and metabolites described above, but is converted into measurement data by quantifying various situations of each part based on in-vivo images output by a measurement device such as CT scan. It is possible to use. Furthermore, in addition to images, it is also possible to measure voices or sounds emitted from the body, digitize them, and use them as measurement data.

DESCRIPTION OF SYMBOLS 1 Detection apparatus 10 Control part 11 Recording part 12 Storage part 13 Input part 14 Output part 15 Acquisition part 11a Detection program

Claims (15)

Based on measurement data of a plurality of factor items obtained by measurement related to a living body, a detection device that detects a candidate for a biomarker that is an index of a symptom of a living body that is a measurement target,
A detection apparatus comprising: a detection unit configured to detect a candidate for the biomarker based on a correlation between a time series change in measurement data of the factor item and a time series change in measurement data between the factor items. It further comprises a differential test means for testing whether the measurement data of the factor item changes with time.
The factor item in the detecting means is a factor item that has been tested to be significant in a change over time by the differential test means.
The detection device according to claim 1. The differential test means performs a test of significance based on a comparison result between measurement data of factor items and preset reference data,
The detection device according to claim 2. The factor item includes at least one of a measurement item relating to a gene, a measurement item relating to a protein, a measurement item relating to a metabolite, and a measurement item relating to an image obtained from a living body,
The detection device according to claim 1. Based on the biomarker candidates detected by the detection means, the presence or absence of an abnormality relating to the living body, whether the living body is in a pre-disease state before transitioning from a normal state to a disease state, and the possibility that the living body will develop Characterized in that it comprises means for outputting information that assists in determining at least one of the diseases having
The detection apparatus of any one of Claims 1-4. Based on measurement data of a plurality of factor items obtained by measurement related to a living body, a detection method using a detection device that detects a candidate for a biomarker that is an indicator of a symptom of a living body that is a measurement target,
A detection method, comprising: detecting a biomarker candidate based on a correlation between a time series change in measurement data of the factor item and a time series change in measurement data between the factor items. A differential test step is performed to test whether the measurement data of the factor item has changed with time with significance;
The factor item in the detection step is a factor item that has been tested to be significant in a temporal change by the differential test step.
The detection method according to claim 6. In the differential test step, a test of significance is performed based on a comparison result between the measurement data of the factor item and preset reference data.
The detection method according to claim 7. The factor item includes at least one of a measurement item relating to a gene, a measurement item relating to a protein, a measurement item relating to a metabolite, and a measurement item relating to an image obtained from a living body,
The detection method of any one of Claims 6-8. Based on the biomarker candidates detected in the detection step, whether there is an abnormality related to the living body, whether the living body is in a pre-disease state before transitioning from a normal state to a disease state, and the living body may develop Performing a step of outputting information for assisting determination regarding at least one of the diseases having sex;
The detection method of any one of Claims 6-9. A detection program for causing a computer to execute a process for detecting a candidate for a biomarker that is an index of a symptom of a living body that is a measurement target, based on measurement data of a plurality of factor items obtained by measurement related to a living body,
On the computer,
A detection program for executing a detection step of detecting a candidate for the biomarker based on a correlation between a time series change in measurement data of the factor item and a time series change in measurement data between the factor items. On the computer,
Performing a differential test step of testing whether the measurement data of the factor item has changed with time with significance,
The factor item in the detection step is a factor item that has been tested to be significant in a temporal change by the differential test step.
The detection program according to claim 11. The differential test step is characterized in that a test of significance is executed based on a comparison result between measurement data of factor items and preset reference data.
The detection program according to claim 12. The factor item includes at least one of a measurement item relating to a gene, a measurement item relating to a protein, a measurement item relating to a metabolite, and a measurement item relating to an image obtained from a living body,
The detection program according to any one of claims 11 to 13. Based on the biomarker candidates detected in the detection step, whether there is an abnormality related to the living body, whether the living body is in a pre-disease state before transitioning from a normal state to a disease state, and the living body may develop A step of outputting information for assisting determination regarding at least one of the diseases having sex;
The detection program of any one of Claims 11-14.