KR20190000168A - System and method for selecting multi-marker panels - Google Patents

Abstract

Disclosed are a system for selecting a multi-marker panel based on a marker connected to a disease-related cell function and a method thereof. The present invention provides a system which is operated by at least one processor for selecting a multi-marker panel including: a step of connecting a disease-related cell function to disease markers based on a disease-related mechanism; a step of extending genes which are similar to at least one disease marker among the disease markers to disease marker candidates; a step of selecting transcriptome markers by using micro-array data and evaluating an expression degree of each of the disease markers and the disease marker candidates; and a step of selecting a multi-marker panel by sharing a function and a mechanism among transcriptome markers and excluding overlapped markers having a similar expression degree.

Description

TECHNICAL FIELD The present invention relates to a system and a method for selecting a multi-marker panel based on a marker linked to disease-related cell function,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to bioinformatics technology, and more specifically, to a biomarker.

Biomarkers, which are disease-specific altering biological markers, have been widely used for medical applications ranging from diagnosis of diseases, prediction of prognosis, diagnosis of drug dependence as well as understanding of biological mechanisms at the cell level of the disease. Markers for disease identification should show a specific pattern between normal and disease, and markers for disease classification should show a specific pattern for each disease type.

Traditionally, disease markers have been discovered through cell-based experiments that alter known phenotypic phenotypes at the cellular level or explored factors that have changed in the disease phenotype. Although these markers can be searched for reliable and causal markers because they find change factors based on their biological functions, it is necessary to construct individual models for each experiment. Marker excavation or verification was difficult.

Since the development of DNA microarray technology, a variety of omix analysis techniques and data production techniques have been investigated, which have led to the development of integrated, multi- Analysis has enabled the discovery of large-scale markers. In particular, transcriptome data, which collectively measure the expression level of RNA-type transcripts transcribed from DNA containing intracellular genetic information, is easier to produce than other omix data in terms of cost and speed It has the most accessible and vast amounts. In the case of microarray data, which is one of the transcript data, many public data and analysis methods are disclosed, and there have been many cases in which marker data were analyzed by analyzing transcript data. In particular, a variety of bioinformatics researches have been conducted based on the data of transcripts extracted from patient groups, and based on them, various diseases such as disease and prognosis diagnosis, disease classification, and new drug development have been studied. In addition, the transcription - based marker model is relatively simple to detect compared to biomaterials such as proteins and metabolites. In fact, 58 nucleic acid-based tests have been cleared or approved by the FDA. Therefore, it is important to identify new transcription markers by analyzing large-scale samples and multiple gene information in transcript data.

However, there are various data sets in the microarray, and the reproducibility of selected markers is low through the analysis of transcript data because the significant genes to be extracted are different in each data set and the overlapping gene ratio in the data sets is not high. Therefore, selection of markers through analysis of transcript data is limited for medical use where reproducibility is important.

It is necessary to analyze a large number of data sets and samples in order to improve the reproducibility of the markers. However, since it is difficult to define the appropriate level of "large number" and many samples may not exist for each disease, Marker selection methods are still limited.

In addition, the markers selected by microarray analysis are not related to cell functions, so it is difficult to know the functions associated with markers alone. Even if a marker is searched based on known information, one gene is related to various mechanisms, The irrelevant function and the marker can be selected.

Such a problem can be detected by selecting a marker based on a cell function associated with a disease, and the disease-specific information can be selected except for information that occurs stochastically in the data and is not reproducible. However, in the previous study, there was no case where the marker was selected through the evaluation of the data set based on the candidate marker based on the cell function.

The problem to be solved by the present invention is to link disease-related cell functions and disease markers based on a disease-related mechanism, to identify new marker candidates similar to disease markers, and then to examine marker candidates by transcript data analysis, And to provide a system and method for selecting a multi-marker panel capable of mechanistic analysis.

There is provided a method of selecting a multi-marker panel, the system being operated by at least one processor according to one embodiment, the method comprising: linking disease-associated cell functions with disease markers on the basis of a mechanism associated with disease; Expanding genes similar to disease markers of disease markers to disease marker candidates, evaluating the degree of expression of each of the disease markers and disease marker candidates using microarray data, and selecting transcript markers, And selecting a multi-marker panel excluding overlapping markers having similar expression patterns while sharing functions and mechanisms among the carcass markers.

The step of linking the disease-related cell functions with the disease markers comprises the steps of searching for disease-associated cell function contained within the disease-associated mechanism pathway, linking disease markers contained within the pathway to disease-related cell function, Extracting an unconnected marker that is not connected to the function, and searching for an established pathway for the unconnected marker, evaluating whether the unconnected markers are significantly included in the cell functions included in the discovered pathway, And linking the unconnected marker to the sought cellular function.

The step of extending to the disease marker candidates includes scoring each of the candidate genes using at least one of relationship similarity to the disease markers, distance and connection strength, and expression correlation, Disease marker candidates can be selected.

The step of expanding to the disease marker candidates may be based on order statistics, and the score of each candidate gene may be calculated by integrating relation similarity score, distance and connection strength score, and expression correlation score on a rank basis based on order statistics have.

Wherein the step of selecting the transcription target markers comprises the steps of calculating the expression magnification and the expression significance probability of the disease markers and the disease marker candidates using the microarray data, Value) is less than or equal to the reference value can be selected as the transcript markers.

Wherein the step of selecting the transcription markers further comprises the step of calculating the discrimination power of each of the disease markers and the disease marker candidates using the same normalized microarray data for each data set, (p value) is less than or equal to the reference value and the discriminating force is equal to or greater than the reference value, can be selected as the transcription markers.

In the step of selecting transcriptional markers, omix markers linked to disease function can be selected by evaluating the verifiability in the tissue or cell line to be verified, among the transcriptional marker.

The step of selecting the multi-marker panel includes classifying markers having similar patterns of expression and sharing function and path among the transcript markers into clusters, removing the markers in each cluster, And selecting a marker combination having an optimum discriminating power as a multi-marker panel while predicting.

A method of selecting a multi-marker panel, the system being operated by at least one processor according to another embodiment, comprising the steps of: linking disease-associated cell functions contained within a disease-associated pathway with disease markers contained within the pathway; If there is an unlinked disease marker that is not linked to the disease-associated cell function among the disease markers stored in the marker database, the pathway for the unconnected disease marker is searched, and disease-related cell functions Linking disease marker to a searched disease-associated cell function, disease markers linked to disease-related cell function, and diseases associated with disease-associated cell function Genes that are similar to at least one disease marker of the markers, Using a step, and the microarray data, which selected as beams, by verifying the disease marker candidates each include the step of selecting a multi-marker panel, in combination with at least a portion of said disease marker marker candidates.

The step of selecting the multi-marker panel may include selecting, among the disease marker candidates, transcription markers whose expression magnification is within a reference range and whose probability of occurrence (p value) is less than or equal to a reference value, have.

In the step of selecting the multi-marker panel, the multi-marker panel may be selected by excluding duplicate markers having similar patterns of expression among the transcriptional markers and sharing function and path.

The step of selecting the disease marker candidates includes scoring each of the candidate genes using at least one of relationship similarity to the disease markers, distance and connection strength, and expression correlation, Genes that are similar to disease markers can be selected.

The step of selecting the disease marker candidates is based on order statistics, and the score of each candidate gene can be calculated by integrating relationship similarity score, distance and connection strength score, and expression correlation score on a rank basis based on order statistics have.

A method for selecting a multi-marker panel, the system being operated by at least one processor according to yet another embodiment, comprising the steps of: receiving disease markers linked to disease-related cell function; identifying at least one disease marker of the disease marker Linking a disease-related cell function of the disease marker to each of the similar genes, selecting genes that are similar to at least one of the disease markers and the disease markers as disease marker candidates, and And verifying each of the disease marker candidates using the microarray data to select a multi-marker panel.

The step of selecting the disease marker candidates includes scoring each of the candidate genes using at least one of relationship similarity to the disease markers, distance and connection strength, and expression correlation, Genes that are similar to disease markers can be selected.

The step of selecting the multi-marker panel includes extracting transcript markers whose expression magnification is within a reference range and whose probability of occurrence (p value) is less than or equal to a reference value and whose discriminating power is not less than a reference value among the disease marker candidates, And selecting the multi-marker panel by excluding duplicate markers having similar expression patterns and sharing function and path among the markers.

According to yet another embodiment, there is provided a multi-marker panel selection system operating by at least one processor, the system comprising: extracting disease markers linked to disease-related cell function on the basis of a disease-associated mechanism; A marker candidate extracting device for extracting similar genes with extended disease markers and using the microarray data to verify each of the disease markers extracted from the marker candidate extracting device and to identify at least some of the disease markers And a marker selection device for selecting a combined multi-marker panel.

Wherein the marker candidate extraction device scores each of the candidate genes on the basis of rank based on at least one of relationship similarity to the disease markers, distance and connection strength, and expression correlation, Genes that are similar to disease markers can be selected.

Wherein the marker selecting device extracts transcript markers whose expression magnification is within a reference range and whose probability of occurrence (p value) is less than or equal to a reference value and whose discriminating power is equal to or greater than a reference value among the inputted disease markers, Markers that share similar functions and paths are classified into clusters. Then, the markers in each cluster are arbitrarily removed, and the discriminative power is predicted based on the remaining markers. .

According to an embodiment of the present invention, markers associated with disease-related cell function can be selected, and disease identification and mechanism analysis can be simultaneously performed. According to the embodiment of the present invention, a marker candidate obtained through linkage of a disease-related cell function and a marker on the basis of a disease mechanism is verified in a plurality of transcript omics to constitute a multi-marker panel, thereby increasing reproducibility in discriminating disease states .

According to the embodiment of the present invention, the selected multi-marker panel can provide a cause analysis or treatment basis of the disease, and can classify the accurate patient group based on the changed function panel.

The selected multi-marker panel according to the embodiment of the present invention can be widely used for disease diagnosis, prognosis prediction, drug accompanied diagnosis, and the like, and can be manufactured with a small-scale microarray or a multiplex analysis kit.

1 is a configuration diagram of a multi-marker panel selection system according to an embodiment of the present invention.
FIG. 2 is a flowchart of a method for associating a marker with a cell function using a marker candidate extracting apparatus according to an embodiment of the present invention.
FIG. 3 is a flowchart of a method for expanding a marker candidate candidate by a marker candidate extracting apparatus according to an embodiment of the present invention.
FIG. 4 is a flowchart of a method of selecting a multi-marker panel according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when an element is referred to as " comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the terms " part, " " module, " and " module ", etc. in the specification mean a unit for processing at least one function or operation and may be implemented by hardware or software or a combination of hardware and software have.

1 is a configuration diagram of a multi-marker panel selection system according to an embodiment of the present invention.

Referring to FIG. 1, the multi-marker panel selection system 10 starts with linking disease-related cell functions and disease markers based on a disease-related mechanism, discovers new marker candidates similar to disease markers, Perform microarray analysis to verify. Because the selected multi-marker panel already maps disease-related cell function and mechanism, disease identification and mechanism analysis are possible at the same time, and limitations of markers discovered based on conventional microarray analysis can be solved.

The multi-marker panel selection system 10 selects a multi-marker panel by integrating biological information similarity analysis and microarray analysis. To this end, the multi-marker panel selection system 10 operates as at least one processor, and includes a biological association-based marker candidate extraction device 100 and a microarray analysis-based marker selection device 200.

The marker candidate extraction apparatus 100 may include a disease-related cell function, a disease marker connection unit 110, and a disease marker candidate extension unit 130. The marker selection apparatus 200 may include a marker evaluation unit 210 and a marker combination selection unit 230.

The multi-marker panel selection system 10 can acquire necessary information in association with the database 300. [ At least some of the databases may be implemented in the multi-marker panel selection system 10, or may be implemented in a remote server. The database 300 includes a disease function and path database 310, a disease marker database 320, a microarray database 330, an interaction and path database 340, a protein and domain function database 350 ), And an intracellular expression and mutation database (360), but it is not necessarily physically separated.

 The marker candidate extracting apparatus 100 includes a disease-related cell function and a disease marker linkage (simply referred to as a 'function-marker linkage') 110 and a disease marker candidate extension unit 130.

The function-marker linkage 110 predicts the relationship between disease-associated cell functions and disease markers based on disease pathway information and acquires disease markers linked to disease-associated cell function. Specifically, the function-marker linker 110 selects a disease pathway that derives a known disease-associated cell function as a final product as a disease mechanism and identifies disease markers upstream of the cell function as markers for disease- . The disease-associated cell function may be extracted from the disease function and path database 310 as known information. The disease marker may be extracted from the disease marker database 320 as known information.

On the other hand, among the disease markers, there may be unconnected markers that have no disease-related cell function. In this case, the function-marker linking unit 110 can predict new disease-associated cell function based on the pathway information on the unconnected marker and generate the relationship between the predicted new disease-associated cell function and the unlinked marker. Whereby the function-marker connection unit 110 can expand the mechanism based on disease markers. Mechanistic path information may be extracted from the interaction and path database 340.

The disease marker candidate expansion unit 130 receives disease markers associated with the disease-related cell function acquired at the function-marker connection unit 110. The disease marker candidate expansion unit 130 extracts genes that are similar to disease markers as candidate markers, and extends markers to be linked to disease-related cell functions.

Specifically, the disease marker candidate expansion unit 130 scores all genes based on the similarity analysis with the disease markers, and compares the genes with the scores of the disease markers to discover genes having similar relationships as candidates for new markers. The similarity analysis method may be various. The disease marker candidate expansion unit 130 compares the various relation information with the disease markers, the distance, the link strength, and the expression pattern, and identifies the significant genes as the new marker candidates.

The disease marker candidate expansion unit 130 extracts a common feature of the disease markers based on various relationship information, and scores a relation score of the candidate genes representing the disease similarity with the disease markers. The various relationship information can include 'action relationship information' such as path-affiliated gene relationship information, transcription factor-target gene relationship information, E3 ubiquitin-substrate relationship information, and scaffold- And may be extracted from the interaction and path database 340. The various relationship information may include various 'function relation information' such as cell function-protein information, domain-protein information, and the like, and may be extracted from the protein and domain function database 350. Cell function-protein information can be used in Gene Ontology database, and domain-protein information can be used in PFAM database.

The disease marker candidate expansion unit 130 can score the network score of the candidate genes indicating the distance and the degree of connection between the disease markers and the candidate genes. The disease marker candidate expansion unit 130 performs link analysis on a network constructed based on protein-protein interaction information, and can score a network score according to the distance and the degree of connection between disease markers and candidate genes. Protein-protein interaction information can be extracted from the interaction and path database 340.

The disease marker candidate expansion unit 130 may analyze the expression correlation of disease markers and candidate genes based on normalized microarray data of a disease of interest and score an expression pattern of the candidate genes. The microarray data may be extracted from the microarray database 330. The microarray database 330 may utilize databases such as Gene Expression Omnibus and ArrayExpress.

The disease marker candidate expansion unit 130 includes a correlation score of candidate genes showing relationship similarity with disease markers, a network score of candidate genes indicating the distance and degree of association between disease markers and candidate genes, and an expression correlation with disease markers Incorporate the expression pattern scores of candidate genes representing the relationship. The integrated score is used as a similarity score for each candidate gene that shows similarity to disease markers. At this time, the disease marker candidate expanding unit 130 may add information of different scales based on order statistics based on rank.

The disease marker candidate expansion unit 130 selects a candidate gene based on the similarity score, evaluates the expression of the selected candidate genes in the target cell or tissue, and selects the candidate as a new disease marker candidate. At this time, the new disease marker candidate is linked to the cell function and mechanism so as to follow the information (disease-related cell function and disease mechanism) linked to the existing disease marker whose similarity is evaluated. Expression in the target cell or tissue can be extracted in an intracellular expression and mutation database 360.

The marker selection device 200 includes a marker evaluation unit 210 and a marker combination selection unit 230.

The marker evaluating unit 210 receives disease marker candidates from the marker candidate extracting apparatus 100, evaluates the degree of expression of each disease marker candidate based on the microarray data, and selects a disease marker. The microarray data is extracted from the microarray database 330. At this time, the marker evaluating unit 210 can calculate fold change and probability of expression (p value, p-value) of each gene included in each microarray data.

The marker evaluating unit 210 selects, for disease markers and disease marker candidates derived from the marker candidate extracting apparatus 100, a marker that can be utilized at the transcript level based on the discrimination power. The marker evaluation unit 210 verifies the discrimination power of individual microarray data for each marker. The marker evaluating unit 210 verifies whether the markers can discriminate the new data through the disease microarray data stored in the microarray database 330. The marker evaluator 210 selects transposable markers that can be used at the transcript level based on the result of the verification and evaluates the verifiability of the tissue or cell line to be verified using the intracellular expression and variation database 360 Select an omix marker associated with disease function.

The marker combination selection unit 230 combines the transcription markers selected by the marker evaluation unit 210 to construct a final multi-marker panel. The marker combination selection unit 230 clusters the transcriptional markers into markers having similar expression patterns while sharing the function and mechanism to remove redundant function markers to generate functional redundant cluster information do. The marker combination selection unit 230 may store the function and the mechanism based on the information on the cell function and the marker collected from the function-marker connection unit 110 and the marker pattern information analyzed by the disease marker candidate expansion unit 130 While clustering markers with similar expression patterns.

The marker combination selection unit 230 performs the disease condition evaluation by removing duplicate markers one by one based on the function redundancy cluster information, thereby constructing an optimal multi-marker panel.

FIG. 2 is a flowchart of a method for associating a marker with a cell function using a marker candidate extracting apparatus according to an embodiment of the present invention.

Referring to FIG. 2, the marker candidate extracting apparatus 100 predicts the relationship between disease-related cell functions and disease markers based on disease pathway information, and obtains disease markers linked to disease-related cell functions.

The marker candidate extraction apparatus 100 searches for a disease-related cell function included in the disease-related pathway, and connects the disease-related marker included in the pathway to the disease-related cell function (S110). The marker candidate extraction device 100 can extract disease-related cell functions from the disease function and path database 310, extract disease markers from the disease marker database 320, and link them through disease pathway information.

The marker candidate extraction device 100 extracts unconnected markers that are not linked to disease-related cell functions among the disease markers (S120).

The marker candidate extracting apparatus 100 searches for pathway information on an unconnected marker, estimates whether the markers are significantly included in the detected pathway and cell functions, and predicts a new function and path of the unconnected marker (S130). The marker candidate extracting apparatus 100 can use the Fisher's exact test to determine the significance of determining whether markers are significantly included in the pathway and cell functions. The marker candidate extraction device 100 can search for the path information in the interaction and path database 340.

The marker candidate extracting apparatus 100 connects the known predicted new function and path to the unconnected marker and extracts a disease marker linked to the final disease-associated cell function (S140).

The disease function and pathway database 310 may include, for example, detailed pathways included in the disease pathway in the KEGG Pathway, BioCyc, WikiPathway, NCI-PID, Reactome database, or other commonly accepted literature such as Hanahan & Weinberg It is possible to map and use the disease mechanisms mentioned above. The disease marker database 320 may include, for example, a marker included in the FDA Nucleic Acid Based Tests, a clinically used marker contained in the NCI Tumor Marker database, a drug target contained in the DrugBank, or a Qiagen Breast Cancer RT2 Profilier PCR Like Array, you can use the information that is being used as a cell experiment marker for disease verification in your company.

The marker candidate extracting apparatus 100 can use the Fisher's exact test to determine the significance of determining whether markers are significantly included in the pathway and cell functions. The contingency table for Fisher's exact verification technique calculation can be summarized as shown in Table 1 below.

Path / Function Associated Genes Pathway / function unrelated gene system Disease marker a b a + b Not a disease marker
gene c d c + d system a + c b + d a + b + c + d

The hypergeometric distributions to be drawn in order to carry out the Fischer's exact test with respect to Table 1 can be obtained by the following equations (1) and (2).

If the p value obtained from Equation (2) has a value of 0.05 or less, it can be said that the disease markers are usually concerned with the path or function. If 30 of the 50 breast cancer markers entered have a mechanism of apoptosis pathway, and 100 of the 20,000 genes have a mechanism of apoptosis, the p value obtained by Fisher's exact test method is 3.27E -58, so that the mechanism can be selected as a new disease mechanism and further feedback on the relationship between mechanism-markers can be provided.

FIG. 3 is a flowchart of a method for expanding a marker candidate candidate by a marker candidate extracting apparatus according to an embodiment of the present invention.

Referring to FIG. 3, the marker candidate extraction apparatus 100 extracts genes that are similar to disease markers linked to disease-related cell function as candidate markers, and extend markers to be linked to disease-related cell functions. The marker candidate extracting apparatus 100 scans a variety of relationship similarities, distance and connection strength, and expression correlation with the extracted disease markers, and identifies significant genes as new marker candidates. Scores for various relational similarities, distances and connection strengths, and expression correlations with extracted disease markers can be calculated in parallel or sequentially.

 The marker candidate extracting apparatus 100 extracts a common feature of the disease markers based on various relation information, and calculates a relation score of the candidate genes representing the disease similarity with the disease markers (S210). Features can be, for example, pathways, transcription factors, E3, skeletal proteins, and the like. At this time, the marker candidate extracting apparatus 100 can use a guilt-by-association method to give a high score to a non-marker gene having a function when many disease markers belong to a certain function. The various relational information may include functional relationship information such as path-affiliated gene relationship information, transcription factor-target gene relationship information, E3 ubiquitase-substrate relationship information, and scaffold- May be extracted from the interaction and path database 340. The various relationship information may include various functional relationship information such as cell function-protein information, domain-protein information, and the like, and may be extracted from the protein and domain function database 350.

The marker candidate extracting apparatus 100 calculates a network score of the candidate genes indicating the distance and the degree of connection between the disease markers and the candidate genes in the network configured based on the protein-protein interaction information (S220). Protein-protein interaction information can be extracted from the interaction and path database 340. The marker candidate extracting apparatus 100 can perform a link analysis in which a score is given based on a connection relation in the graph data in such a manner that the closer the marker is to the marker and the more markers are placed around the marker, .

The marker candidate extracting apparatus 100 analyzes the expression correlation of each disease marker and each candidate gene based on the disease transcript data stored in the microarray database 330 and calculates an expression pattern score of the candidate genes (S230) . Genes with similar expression patterns are scored to reflect those likely to have a common function.

The marker candidate extracting apparatus 100 integrates scores of candidate genes obtained through various methods (S240). At this time, the marker candidate extracting apparatus 100 integrates information of different scales on the basis of order statistics. The marker candidate extracting apparatus 100 may integrate information having different scales as shown in Equation (3).

In Equation (3), each r value is a rank ratio (rank number divided by the total number of genes) of the gene obtained in each scoring method, and N is the number of total scored methods. The ranking-based score integration method is advantageous in that it can be integrated regardless of the missing value because it is calculated except for methods that can not be scored because there is no information. In addition, the ranking-based score integration method has a merit of being highly scalable since it can be easily calculated by adding only the ranking ratio value when a new scoring method is devised.

The marker candidate extracting apparatus 100 extracts candidate genes capable of performing similar to known disease markers based on the integration score of the candidate genes (similarity score of the candidate gene indicating similarity with the disease markers) (S250). The marker candidate extracting apparatus 100 confirms the distribution of scores of known disease markers, selects an index capable of performing as much as disease markers based on the integrated score of the candidate genes, Candidates can be selected. For example, the marker candidate extracting apparatus 100 can select genes in the third quantile of the existing marker ranks as new disease marker candidates.

In step S210, the marker candidate extracting apparatus 100 calculates a p value for each feature (e.g., a path, a transcription factor, E3, a skeletal protein, etc.) through the Fischer's exact test of Equations 1 and 2 do. The marker candidate extracting apparatus 100 integrates the p values for each data through the Fisher's method as shown in Equation (4).

In Equation (4), p _i means p values in the data. The more common the gene with the lower p value, the higher the significance.

In step S220, the marker candidate extraction apparatus 100 may score the network similarity-based marker significance through a method of Random Walk with Restart (RWR).

In step S230, the marker candidate extracting apparatus 100 can analyze the expression correlation of each disease marker and each candidate gene through a Pearson's correlation coefficient. The marker candidate extracting apparatus 100 obtains a p value for a marker-gene pair having a Pearson correlation coefficient of 0.7 or more or -0.7 or less and calculates p values indicating the significance of the correlation with each marker for each candidate gene, Integrate through the Fisher method as in Equation 4.

FIG. 4 is a flowchart of a method of selecting a multi-marker panel according to an embodiment of the present invention.

Referring to FIG. 4, the marker selecting apparatus 200 receives disease marker candidates (candidate genes) and evaluates the degree of expression of each disease marker candidate based on the microarray data (S310). The marker selection device 200 calculates the change in expression magnification and p value between the disease-normal or disease state of each disease marker candidate gene using the transcript data stored in the microarray database 330. It is highly probable that the expression markers will be different if the expressions vary greatly depending on the conditions, the distribution of the expression amount is dense, the expression magnification increases, and the expression probability p value is small. The marker selection device 200 can evaluate the degree of expression using the toptable function of the R limma package.

The marker selecting apparatus 200 extracts a transcript marker which can be detected at the nucleic acid level based on the expression level of each disease marker candidate (S320). The marker selection device 200 can extract markers with a p-value of 0.05 or less, with an expression magnification of more than 2 or less than 0.5, with a disease transcript marker.

The marker selecting apparatus 200 verifies the discriminating power of each transcription marker using the transcript data stored in the microarray database 330 (S330). The marker selection device 200 can perform leave-one-out cross validation. The marker selection apparatus 200 can identify and verify the discrimination criterion based on the same normalized data for each data set, and select a marker having a discrimination power of 70% or more as a discriminative marker. Meanwhile, the marker selecting apparatus 200 can perform the normalization of the reference gene center divided by the sum of the expression amounts of the housekeeping genes HPRT, GAPDH, ACTB, and GUSB. As a method for solving the problem that conventional quantile normalization can not be utilized due to a small number of verifications in the marker panel rather than a microarray level verification number when the multi-marker panel is finally constructed, The controller 200 performs normalization as if analyzing actual marker panel data.

The marker selection device 200 evaluates the verifiability of the tissue or cell line to be verified among the transcriptional markers having discriminating power, and selects an omix marker associated with the disease function (S340). The marker selection device 200 can utilize the intracellular expression and variation database 360 to evaluate the verifiability of a tissue or cell line. The intracellular expression and mutation database 360 can utilize the gene / protein expression information of The Human Protein Atlas and the patient and cell line gene mutation information of COSMIC. The marker selection device 200 may exclude the markers having mutation in the cell line to be verified from the final selection, except for the markers having no expression in the tissue or cell line to be verified, among the transcriptional markers having the discriminating power.

The marker selecting apparatus 200 clusters genes having similar patterns of expression among the selected markers and sharing function and path (S350). The marker selection device 200 utilizes the function and path information in the disease function and path database 310, the marker information in the disease marker database 320, and the transcript data information in the microarray database 330, Genes that share similar functions and pathways can be clustered. The marker selection device 200 can analyze the expression pattern similarity through Pearson's correlation coefficient.

The marker selecting apparatus 200 arbitrarily removes the markers in each cluster and constructs a discriminator based on the remaining markers to predict the discrimination power, and selects a marker combination having the best discriminating power as a multi-marker panel (S360). The marker selection device 200 determines the combination of the markers having the best discrimination power by removing the markers one by one in the direction in which the discrimination power is high. The marker selection device 200 may constitute a support vector machine (SVM) discriminator for discriminating marker expression data for each disease state in the microarray data.

As described above, the multi-marker panel selection system 10 of the present invention assigns and mutually feedbacks known markers to disease-related cell functions, expands new marker candidates through similarity analysis based on the markers, And the minimum and optimal multi-marker panel can be selected by eliminating overlapping markers.

In particular, the present invention extends new mechanisms based on markers associated with disease mechanisms and adds new markers through similarity analysis, network analysis, and expression pattern similarity analysis based on disease markers. In addition, the present invention evaluates the discriminative power of individual markers considering the normalization method to be applied when using the marker panel, and can optimize the multi-marker panel by reducing the marker combination based on the expression pattern and the mechanism similarity.

The conventional marker selection method based on microarray data analysis has a disadvantage in that the predictive power is not constant because the variation of marker significance is large depending on the data set. In addition, existing marker selection methods based on microarray data analysis have difficulties in knowing the exact function of the selected markers and in some cases, genes that are not related to the disease function are wrongly selected. In the conventional microarray data analysis, the selected marker did not know the cell function and mechanism, and it was necessary to simply map the cell function to the selected marker at a later stage. However, the present invention can be applied to a multi- Marker panel can be selected.

The embodiments of the present invention described above are not implemented only by the apparatus and method, but may be implemented through a program for realizing the function corresponding to the configuration of the embodiment of the present invention or a recording medium on which the program is recorded.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, It belongs to the scope of right.

Claims (19)

A method for selecting a multi-marker panel by a system operating by at least one processor,
Linking disease-related cell functions to disease markers based on disease-related mechanisms,
Expanding genes that are similar to at least one disease marker of the disease markers to disease marker candidates,
Evaluating the degree of expression of each of the disease markers and the disease marker candidates using the microarray data, selecting transcription marker
A step of selecting a multi-marker panel excluding duplicate markers having similar expression patterns and sharing functions and mechanisms among the transcriptional markers
The method comprising the steps of: The method of claim 1,
The step of linking disease-associated cell functions with disease markers
Detecting disease-associated cell function contained within the disease-associated pathway, linking disease markers contained within the pathway to disease-associated cell function,
Extracting unconnected markers that are not linked to disease-associated cell function, and
Searching for a pathway for the unconnected marker, evaluating whether the unconnected markers are significantly included in the cell functions contained in the discovered pathway, and linking the unconnected marker to the searched cell function
The method comprising the steps of: The method of claim 1,
The step of extending to the disease marker candidates
Marker markers for scoring each of the candidate genes using at least one of relationship similarity to the disease markers, distance and linking intensity, and expression correlation, and selecting the disease marker candidates based on the score of each candidate gene Selection method. 4. The method of claim 3,
The step of extending to the disease marker candidates
A method for selecting a multi-marker panel to calculate score of each candidate gene based on order statistics, based on rank, based on ranking similarity score, distance and connection score, and expression correlation score. The method of claim 1,
The step of selecting transcriptional marker
Calculating marker magnitudes and probability expressions of the disease markers and the disease marker candidates using the microarray data and setting the markers whose expression magnification is within the reference range and the probability of occurrence expression (p value) A method of selecting a multi-marker panel to select from carcass markers. The method of claim 5,
The step of selecting transcriptional marker
The discrimination power of each of the disease markers and the disease marker candidates is further calculated using the same normalized microarray data for each data set, and the expression magnification is within a reference range and the probability of occurrence expression (p value) And selecting the markers having the discriminating power equal to or greater than the reference value as the transcription markers. The method of claim 5,
The step of selecting transcriptional marker
A method for selecting a multi-marker panel for selecting an omics marker associated with a disease function by evaluating the verifiability of a tissue or cell line to be verified among the transcription marker markers. The method of claim 1,
The step of selecting the multi-marker panel
Classifying markers having similar expression patterns and sharing functions and paths among the transcript markers into clusters; and
A step of selecting a marker combination having an optimal discriminating power as a multi-marker panel while arbitrarily removing markers in each cluster and predicting discrimination power based on the remaining markers
The method comprising the steps of: A method for selecting a multi-marker panel by a system operating by at least one processor,
Linking the disease-associated cell function contained within the disease-associated pathway with disease markers contained within the pathway,
If there is an unconnected disease marker that is not linked to disease-associated cell function among the disease markers stored in the disease marker database, the pathway for the unconnected disease marker is searched for and the disease-associated cell function Link disease marker to the searched disease-associated cell function by assessing whether the non-linked disease marker is significantly included in the disease-associated cell function,
Selecting disease marker candidates as genes that are similar to disease markers of at least one of disease markers linked to disease-related cell function and disease markers linked to disease-related cell function, and
A step of verifying each of the disease marker candidates using microarray data and selecting a multi-marker panel combined with at least some markers of the disease marker candidates
The method comprising the steps of: The method of claim 9,
The step of selecting the multi-marker panel
Wherein the transcription marker having an expression magnification ratio within a reference range and a probability of occurrence (p value) of less than or equal to a reference value and a discrimination power equal to or greater than a reference value is selected by the multi-marker panel among the disease marker candidates. 11. The method of claim 10,
The step of selecting the multi-marker panel
And selecting the multi-marker panel by excluding redundant markers having similar expression patterns and sharing function and path among the transcript markers. The method of claim 9,
The step of selecting as the disease marker candidates
Wherein each of the candidate genes is scored using at least one of relationship similarity to the disease markers, distance and connection intensity, and expression correlation, and a plurality of genes, which are similar to the disease marker, are selected based on the score of each candidate gene How to select marker panel. The method of claim 12,
The step of selecting as the disease marker candidates
A method for selecting a multi-marker panel to calculate score of each candidate gene based on order statistics, based on rank, based on ranking similarity score, distance and connection score, and expression correlation score. A method for selecting a multi-marker panel by a system operating by at least one processor,
Receiving disease markers associated with disease-associated cell function,
Extracting genes similar to at least one disease marker of the disease markers,
Linking the disease-associated cell function of the disease marker to each of the similar genes,
Selecting genes that are similar to at least one of the disease markers and the disease markers as disease marker candidates, and
A step of verifying each of the disease marker candidates using the microarray data to select a multi-marker panel
The method comprising the steps of: The method of claim 14,
The step of selecting as the disease marker candidates
Wherein each of the candidate genes is scored using at least one of relationship similarity to the disease markers, distance and connection intensity, and expression correlation, and a plurality of genes, which are similar to the disease marker, are selected based on the score of each candidate gene How to select marker panel. The method of claim 14,
The step of selecting the multi-marker panel
Extracting transcript markers whose expression magnification is within a reference range and whose probability of occurrence (p value) is less than or equal to a reference value and whose discriminating power is equal to or greater than a reference value among the disease marker candidates, and
Selecting the multi-marker panel by excluding duplicate markers having similar expression patterns and sharing function and path among the transcript markers
The method comprising the steps of: A multi-marker panel selection system operated by at least one processor,
A marker candidate extraction device extracting disease markers linked to disease-related cell function based on a disease-related mechanism, extracting genes similar to at least one disease marker of the disease markers into extended disease markers, and
A marker selection device for verifying each of the disease markers extracted from the marker candidate extraction device using the microarray data and selecting a multi-marker panel combined with at least some of the disease markers
A multi-marker panel selection system. The method of claim 17,
The marker candidate extracting apparatus
Wherein each of the candidate genes is scored on the basis of ranking using at least one of relationship similarity to the disease markers, distance and connection intensity, and expression correlation, and genes similar to the disease marker based on the score of each candidate gene Multi-marker panel selection system to select. The method of claim 17,
The marker selection device
Among the inputted disease markers, transcription markers having an expression magnification ratio within a reference range and a probability of occurrence (p value) of less than or equal to a reference value and a discriminating power equal to or greater than a reference value are extracted,
Markers having similar expression patterns and sharing functions and paths among the transcript markers are classified into clusters,
A multi-marker panel selection system that selects a marker combination with optimal discriminant power as a multi-marker panel while arbitrarily removing markers in each cluster and predicting discrimination based on remaining markers.

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