CN107133492B - Method for identifying gene pathway based on PAGES - Google Patents
Method for identifying gene pathway based on PAGES Download PDFInfo
- Publication number
- CN107133492B CN107133492B CN201710300778.8A CN201710300778A CN107133492B CN 107133492 B CN107133492 B CN 107133492B CN 201710300778 A CN201710300778 A CN 201710300778A CN 107133492 B CN107133492 B CN 107133492B
- Authority
- CN
- China
- Prior art keywords
- gene
- weight
- frequency
- sequenced
- maximum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 108090000623 proteins and genes Proteins 0.000 title claims abstract description 293
- 230000037361 pathway Effects 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000019491 signal transduction Effects 0.000 claims abstract description 12
- 238000012163 sequencing technique Methods 0.000 claims abstract description 8
- 238000012937 correction Methods 0.000 claims description 11
- 230000014509 gene expression Effects 0.000 description 12
- 230000003993 interaction Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 108700005081 Overlapping Genes Proteins 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010199 gene set enrichment analysis Methods 0.000 description 2
- 230000037353 metabolic pathway Effects 0.000 description 2
- 238000003068 pathway analysis Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 239000013585 weight reducing agent Substances 0.000 description 2
- 108090000790 Enzymes Proteins 0.000 description 1
- 102000004190 Enzymes Human genes 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 108700005075 Regulator Genes Proteins 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000005842 biochemical reaction Methods 0.000 description 1
- 230000008236 biological pathway Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000010209 gene set analysis Methods 0.000 description 1
- 238000013090 high-throughput technology Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000002493 microarray Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000001558 permutation test Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Engineering & Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- Biophysics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Epidemiology (AREA)
- Artificial Intelligence (AREA)
- Bioethics (AREA)
- Crystallography & Structural Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Chemical & Material Sciences (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Software Systems (AREA)
- Genetics & Genomics (AREA)
- Molecular Biology (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The embodiment of the invention discloses a method for identifying a gene pathway based on PAGES, which comprises the steps of obtaining a sample, determining a signal pathway and a gene of the sample, and further obtaining the gene frequency and the gene out degree of each gene; counting the maximum gene frequency, the minimum gene frequency, the maximum gene out-degree and the minimum gene out-degree according to the gene frequency and the gene out-degree of each gene, and obtaining the gene frequency weight and the gene out-degree weight of each gene; and according to the gene frequency weight and the gene out-degree weight of each gene, calculating the comprehensive weight of each gene, obtaining the weight of each signal path, further sequencing the weights of each signal path, and determining that the probability of the signal path corresponding to the maximum signal path weight is the maximum. By implementing the embodiment of the invention, the pathway is identified by combining the importance and the specificity of the gene, and the identification precision of the pathway is improved.
Description
Technical Field
The invention relates to the technical field of system biology research, in particular to a method for identifying a gene pathway based on PAGES.
Background
The high-throughput technology based on microarray generates a large amount of gene expression data, how to gain insight from the large amount of gene expression data, and further understanding the mechanism of life phenomena remains a serious challenge to scientists around the world. Biological pathways are the interaction between a group of genes that fulfill specific functions, mainly signaling pathways and metabolic pathways. In a signaling pathway, a node represents a gene (or gene product) and an edge represents a signal that is transduced from one gene to another. In a metabolic pathway, nodes represent biochemical compounds and edges represent biochemical reactions between compounds encoded by enzymes that are encoded by genes. Common pathway databases are the KEGG and Reactome databases, which provide a visualization format for interactions between genes.
From the perspective of system biology, the interaction between genes and the change of their kinetics are the main causes of various diseases and cancers, and since the topological features of the pathway reflect the position, importance and interaction between genes of the genes in the pathway, the pathway should be identified by considering as much as possible various information of the genes contained in the pathway, such as the upstream and downstream positions of the genes, the number of regulatory genes, the interaction relationship between genes, and the like.
In 2005, PNAS published two important approaches to pathway analysis, one is a significant pathway analysis method based on function proposed by Tian et al, which comprehensively considers the significance of the difference between gene expression in a gene set and gene expression outside the set (row replacement) and the significance of the correlation between gene expression of the gene set and phenotype (column replacement). Another is the well-known GSEA method, a gene set enrichment analysis method, proposed by Subramanian et al, whose main idea is to rank all genes according to their correlation between gene expression in a pathway and a given phenotype, and then determine the score for the degree to which the Kolmogorov-Smirnov (Schmilnorov) statistic for a given pathway P is close to extreme in the ranked list. In this method, the significance of the Kolmogorov-Smirnov statistic was determined from the column permutation of the samples. In 2006, Zahn et al used the Van der Waerden statistic instead of the Kolmogorov-Smirnov statistic and replaced the permutation test method with bootstrap sampling that takes into account the correlation of the expression levels of the two genes in the pathway and the correlation with other factors. In the same year EFRON et al used the max-mean statistic instead of the Kolmogorov-Smirnov statistic to calculate the pathway score, then normalized the score by the row permutation method, and finally tested the significance of the pathway score by the column permutation, which is the well-known GSA method.
On the basis of the above-mentioned gene set enrichment analysis method GSEA and gene set analysis method GSA, the scholars also propose a signal pathway influence analysis method SPIA and an overlapping gene weight reduction method PADOG. In the signal pathway influence analysis method SPIA, only the influence of the upstream and downstream positions of genes on the propagation of a perturbation signal is considered, but genes which regulate a large number of genes in a pathway are ignored to be more important than genes which regulate a small number of genes, and the difference has greater influence on the function of the pathway, while in the overlapping gene weight reduction method PADOG, the influence of "common genes" which frequently appear in many pathways is reduced on the basis of the GSA method, but the genes which regulate a large number of genes in the pathway are not considered to be more important than genes which regulate a small number of genes, and the difference has greater influence on the function of the pathway.
Therefore, it is necessary to define the number of genes that a gene regulates downstream in a pathway as the gene-out degree and as the gene importance, and the number of times a gene appears in a pathway as the gene frequency and as the gene specificity, so as to improve the identification accuracy of a pathway by combining the gene definition importance and specificity.
Disclosure of Invention
The embodiment of the invention aims to provide a method for identifying a gene path based on PAGES, which identifies the path by combining the importance and the specificity of the gene and improves the identification precision of the path.
In order to solve the above technical problems, an embodiment of the present invention provides a method for identifying a gene pathway based on PAGIS, the method including:
a. obtaining a sample, determining signal paths of the sample and genes contained in each signal path, sequencing the genes contained in all the signal paths according to the correlation between each gene and a phenotype, and further determining the gene frequency and the gene out-degree of each gene according to the sequenced genes; wherein the gene frequency is the total number of times a gene appears in the determined signal pathway, and the gene out degree is the number of genes which regulate and control downstream genes in the determined signal pathway;
b. counting the maximum gene frequency, the minimum gene frequency, the maximum gene output and the minimum gene output according to the obtained gene frequency and the gene output of each gene, obtaining the gene frequency weight of each gene according to the counted maximum gene frequency and the counted minimum gene frequency, and obtaining the gene output weight of each gene according to the counted maximum gene output and the counted minimum gene output;
c. determining the total number of genes contained in each signal channel and the correction score of each sequenced gene, and calculating the channel score of each signal channel according to the total number of the genes contained in each signal channel, the correction score of each sequenced gene and the corresponding gene frequency weight;
d. and calculating the comprehensive weight of each gene according to the obtained gene frequency weight of each gene and the corresponding gene out-degree weight of each gene, revising the correspondingly calculated channel score of each signal channel according to the calculated comprehensive weight of each gene, further sequencing the revised channel score of each signal channel, and determining that the probability of the signal channel corresponding to the maximum channel score after sequencing is the maximum.
Wherein, the step b specifically comprises:
according to the formula
Obtaining the gene frequency weight of each gene; wherein, f (g)j) Is sequenced gene gjThe frequency of the gene(s); w is af(gj) Is sequenced gene gjFrequency of gene(s)A weight;
according to the formula
Obtaining the gene out-degree weight of each gene; wherein d (g)j) Is sequenced gene gjGene outbreak of (2); w is ad(gj) Is sequenced gene gjGene out-degree weight of (c).
Wherein the value range of the gene frequency weight of each gene is [1, 2 ].
Wherein the value range of the gene out-degree weight of each gene is [1, 2 ].
Wherein, the 'comprehensive weight of each gene' in the step d is determined by a formula
To realize the operation; wherein, w (g)j) Is sequenced gene gjThe integrated weight of (2).
Wherein, the value range of the comprehensive weight of each gene is [1, 2 ].
Wherein, the channel scores of the signal channels calculated correspondingly in the step d are all revised through the formula
To realize the operation; wherein, ES0(S) is sequenced gene gjThe path fraction of the signal path S; m is sequenced gene gjThe total number of genes contained in the signal path S; t (g)j) Is sequenced gene gjThe correction score of (1).
The embodiment of the invention has the following beneficial effects:
in the embodiment of the present invention, the gene frequency weight (i.e., the specificity of the gene) and the gene out-degree weight (i.e., the importance of the gene) of the gene are counted, and the integrated weight of the gene is calculated by combining the two weights, so that the weight of the signal path is determined and the importance of the path is identified by the weight of the signal path, thereby achieving the purpose of improving the accuracy of the identification of the path.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
FIG. 1 is a flowchart of a method for identifying a gene pathway based on PAGES according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a method for identifying a gene pathway based on PAGIS is provided in an embodiment of the present invention, the method comprising:
step S1, obtaining a sample, determining the signal path of the sample and the gene contained in each signal path, sequencing the genes contained in all the signal paths according to the correlation between each gene and the phenotype, and further determining the gene frequency and the gene out-degree of each gene according to the sequenced genes; wherein the gene frequency is the total number of times a gene appears in the determined signal pathway, and the gene out degree is the number of genes which regulate and control downstream genes in the determined signal pathway;
the specific process comprises the steps of obtaining a sample, determining signal paths of the sample and genes contained in each signal path, and further determining the gene frequency distribution and the gene outbreak distribution of the genes. The frequency of occurrence of genes in a pathway (i.e., gene frequency) actually reflects the specificity of a gene, genes frequently occurring in many pathways belong to "common genes" whose influence on the pathway is relatively small, whereas genes occurring only in one or several pathways have high specificity and their differential expression has a large influence on the pathway. Similarly, the expression of gene expression indicates the number of downstream genes regulated by a gene, and thus the larger the expression of gene expression, the greater the influence on the pathway.
Meanwhile, the genes contained in all signal paths are also ordered according to the correlation between each gene and the phenotype, so that the correction scores among the genes can be counted. Assuming a total number of all genes N, a signaling pathway S is given with a base factor M, N genes are ordered by r (or t statistic) as the correlation between each gene g and the phenotype1,...,gj,...gN]。
Step S2, counting the maximum gene frequency, the minimum gene frequency, the maximum gene output and the minimum gene output according to the obtained gene frequency and gene output of each gene, obtaining the gene frequency weight of each gene according to the counted maximum gene frequency and minimum gene frequency, and obtaining the gene output weight of each gene according to the counted maximum gene output and minimum gene output;
the specific process is that according to the gene frequency and the gene out degree of each obtained gene, the maximum gene frequency max (f), the minimum gene frequency min (f), the maximum gene out degree max (d) and the minimum gene out degree min (d) are counted;
according to the formula
Obtaining the gene frequency weight of each gene; wherein, f (g)j) Is sequenced gene gjThe frequency of the gene(s); w is af(gj) Is sequenced gene gjThe weight of gene frequency of (a), the value reflecting the degree of specificity of the gene in the pathway, the greater the value, the higher the degree of specificity of the gene in the pathway, and vice versa, the lower the degree of specificity, wf(gj) Is in the range of [1, 2]]In between, i.e., the value range of the gene frequency weight of each gene is [1, 2]]；
According to the formula
Obtaining the gene out-degree weight of each gene; wherein d (g)j) Is sequenced gene gjGene outbreak of (2); w is ad(gj) Is sequenced gene gjThe gene out-degree weight of (a), the value reflecting the importance of the gene in the pathway, the greater the value, the higher the importance of the gene in the pathway; conversely, the less important the gene is in the pathway, wd(gj) Is in the range of [1, 2]]In between, i.e., the out-degree weight of each gene is in the range of [1, 2]]。
Step S3, determining the total number of genes contained in each signal path and the correction score of each sequenced gene, and calculating the path score of each signal path according to the total number of the genes contained in each signal path, the correction score of each sequenced gene and the corresponding gene frequency weight;
the specific process is that the weighted absolute correction score sum of all genes in the signal path is used for calculating the path score of each signal path, namely the path score of each signal path can be calculated by a formula
To effect the calculation of the path fraction for each signal path; wherein, ES0(S) is sequenced gene gjThe path fraction of the signal path S; m is sequenced gene gjThe total number of genes contained in the signal path S; t (g)j) Is sequenced gene gjThe correction score of (1).
Step S4, calculating a comprehensive weight of each gene according to the obtained gene frequency weight of each gene and the corresponding gene out-degree weight thereof, revising the path score of the corresponding calculated signal path according to the calculated comprehensive weight of each gene, further ranking the revised path score of each signal path, and determining that the probability of the signal path corresponding to the ranked maximum path score is the largest.
The specific process isCombining the gene frequency weight and the gene out-degree weight through a formula
The comprehensive weight w (g) of each gene was calculatedj) (ii) a Wherein, w (g)j) Is sequenced gene gjThe value of (a) reflects the degree of importance and specificity of the gene in the pathway, the higher the degree of importance and specificity of the gene in the pathway, the larger the value, and the lower the degree of importance or specificity of the gene, and w (g)j) Is in the range of [1, 2]]In between, i.e., the value range of the integrated weight of each gene is [1, 2]]。
The resultant integrated weight w (g) of each genej) Substitution of Gene frequency weight wf(gj) The pathway score of each gene is revised by formula
The revision of the gene channel scores is realized, the revised channel scores are further sorted from large to small, and the probability that the signal channel corresponding to the sorted maximum channel score has the maximum change is determined, namely the higher the ranking of the channel scores is, the higher the signal channel tendency is, the higher the research value is.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the present invention, the gene frequency weight (i.e., the specificity of the gene) and the gene out-degree weight (i.e., the importance of the gene) of the gene are counted, and the integrated weight of the gene is calculated by combining the two weights, so that the weight of the signal path is determined and the importance of the path is identified by the weight of the signal path, thereby achieving the purpose of improving the accuracy of the identification of the path.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A method for identifying a gene pathway based on PAGIS, the method comprising:
a. obtaining a sample, determining signal paths of the sample and genes contained in each signal path, sequencing the genes contained in all the signal paths according to the correlation between each gene and a phenotype, and further determining the gene frequency and the gene out-degree of each gene according to the sequenced genes; wherein the gene frequency is the total number of times a gene appears in the determined signal pathway, and the gene out degree is the number of genes which regulate and control downstream genes in the determined signal pathway;
b. counting the maximum gene frequency, the minimum gene frequency, the maximum gene output and the minimum gene output according to the obtained gene frequency and the gene output of each gene, obtaining the gene frequency weight of each gene according to the counted maximum gene frequency and the counted minimum gene frequency, and obtaining the gene output weight of each gene according to the counted maximum gene output and the counted minimum gene output;
c. determining the total number of genes contained in each signal channel and the correction score of each sequenced gene, and calculating the channel score of each signal channel according to the total number of the genes contained in each signal channel, the correction score of each sequenced gene and the corresponding gene frequency weight;
d. calculating the comprehensive weight of each gene according to the obtained gene frequency weight of each gene and the corresponding gene out-degree weight of each gene, revising the path score of the corresponding calculated signal path according to the calculated comprehensive weight of each gene, further sequencing the revised path score of each signal path, and determining that the probability of the signal path corresponding to the maximum path score after sequencing is the maximum;
the step b specifically comprises the following steps:
counting the maximum gene frequency max (f), the minimum gene frequency min (f), the maximum gene frequency max (d) and the minimum gene frequency min (d) according to the obtained gene frequency and gene output of each gene;
according to the formula
Obtaining the gene frequency weight of each gene; wherein, f (g)j) Is sequenced gene gjThe frequency of the gene(s); w is af(gj) Is sequenced gene gjGene frequency weight of (2);
according to the formula
Obtaining the gene out-degree weight of each gene; wherein d (g)j) Is sequenced gene gjGene outbreak of (2); w is ad(gj) Is sequenced gene gjGene out-degree weight of (a);
the 'comprehensive weight of each gene' in the step d is determined by a formula
To realize the operation; wherein, w (g)j) Is sequenced gene gjThe integrated weight of (2).
2. The method of claim 1, wherein the gene frequency weight for each gene is in the range of [1, 2 ].
3. The method of claim 1, wherein the out-of-degree weight for each gene is in the range of [1, 2 ].
4. The method of claim 1, wherein the integrated weight for each gene is in the range of [1, 2 ].
5. The method of claim 1, wherein the step d of revising the path scores of the respective calculated signal paths is represented by the formula
To realize the operation; wherein, ES0(S) is sequenced gene gjThe path fraction of the signal path S; m is sequenced gene gjThe total number of genes contained in the signal path S; gamma (g)j) Is sequenced gene gjThe correction score of (1).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710300778.8A CN107133492B (en) | 2017-05-02 | 2017-05-02 | Method for identifying gene pathway based on PAGES |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710300778.8A CN107133492B (en) | 2017-05-02 | 2017-05-02 | Method for identifying gene pathway based on PAGES |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107133492A CN107133492A (en) | 2017-09-05 |
| CN107133492B true CN107133492B (en) | 2020-08-25 |
Family
ID=59715349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710300778.8A Expired - Fee Related CN107133492B (en) | 2017-05-02 | 2017-05-02 | Method for identifying gene pathway based on PAGES |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107133492B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108763862B (en) * | 2018-05-04 | 2021-06-29 | 温州大学 | Method for deducing gene pathway activity |
| CN109063415A (en) * | 2018-06-08 | 2018-12-21 | 温州大学 | A kind of method of defined function sub-channel level of disruption |
| CN109817337B (en) * | 2019-01-30 | 2020-09-08 | 中南大学 | Method for evaluating channel activation degree of single disease sample and method for distinguishing similar diseases |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1232280A2 (en) * | 1999-11-12 | 2002-08-21 | Fraunhofer-Gesellschaft Zur Förderung Der Angewandten Forschung E.V. | Method for evaluating states of biological systems |
| EP1086240A4 (en) * | 1998-06-19 | 2005-07-27 | Rosetta Inpharmatics Inc | Methods for testing biological network models |
| CN103093119A (en) * | 2013-01-24 | 2013-05-08 | 南京大学 | Method for recognizing significant biologic pathway through utilization of network structural information |
| CN103995983A (en) * | 2014-06-09 | 2014-08-20 | 中国人民解放军国防科学技术大学 | Method for analyzing node importance in signal transduction network based on logic model |
-
2017
- 2017-05-02 CN CN201710300778.8A patent/CN107133492B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1086240A4 (en) * | 1998-06-19 | 2005-07-27 | Rosetta Inpharmatics Inc | Methods for testing biological network models |
| EP1232280A2 (en) * | 1999-11-12 | 2002-08-21 | Fraunhofer-Gesellschaft Zur Förderung Der Angewandten Forschung E.V. | Method for evaluating states of biological systems |
| CN103093119A (en) * | 2013-01-24 | 2013-05-08 | 南京大学 | Method for recognizing significant biologic pathway through utilization of network structural information |
| CN103995983A (en) * | 2014-06-09 | 2014-08-20 | 中国人民解放军国防科学技术大学 | Method for analyzing node importance in signal transduction network based on logic model |
Non-Patent Citations (6)
| Title |
|---|
| Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes;Zuguang Gu et al;《BMC Systems Biology》;20120606;第6卷(第1期);1-13 * |
| Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structure;Reuben Thomas et al;《Genome Biology》;20090424;1-15 * |
| Down-weighting overlapping genes improves gene set analysis;Adi Tarca et al;《BMC Bioinfomatics》;20120630;1-15 * |
| Pathway analysis of differentially expressed genes in human esophageal squamous cell carcinoma;C.Q.XU et al;《European Review for Medical and Pharmacological Sciences》;20151231;1652-1661 * |
| 基因表达谱中信号通路基因集分析方法进展;张威等;《生物技术通讯》;20130331;第24卷(第2期);258-261 * |
| 复杂生物网络中的稠密子图挖掘算法研究;张航;《中国优秀硕士学位论文全文数据库》;20120315;第2012年卷(第3期);I138-2368 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107133492A (en) | 2017-09-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pelossof et al. | Prediction of potent shRNAs with a sequential classification algorithm | |
| Liu et al. | Network analyses in microbiome based on high-throughput multi-omics data | |
| Lamarre et al. | Optimization of an RNA-Seq differential gene expression analysis depending on biological replicate number and library size | |
| Caudai et al. | AI applications in functional genomics | |
| Yuan et al. | Patient-specific data fusion defines prognostic cancer subtypes | |
| Dopazo | Functional interpretation of microarray experiments | |
| Boulesteix et al. | Added predictive value of high-throughput molecular data to clinical data and its validation | |
| CN107133492B (en) | Method for identifying gene pathway based on PAGES | |
| Huo et al. | P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher’s meta-analysis method in omics applications | |
| Zeng et al. | Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models | |
| JP2023544067A (en) | Basal medium development method and system | |
| Mogensen et al. | A random forest approach for competing risks based on pseudo‐values | |
| Ran et al. | scDoc: correcting drop-out events in single-cell RNA-seq data | |
| Duren et al. | Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data | |
| Kundaje et al. | A predictive model of the oxygen and heme regulatory network in yeast | |
| CN107220526B (en) | Method for identifying gene pathway based on PADOG | |
| Sirohi et al. | Identification of drought stress-responsive genes in rice (Oryza sativa) by meta-analysis of microarray data | |
| Wang et al. | KmerGO: a tool to identify group-specific sequences with k-mers | |
| CN107203704B (en) | Method for identifying gene pathway based on GSA | |
| Pradhan et al. | miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles | |
| Lv et al. | Alternative splicing events and splicing factors are prognostic in adrenocortical carcinoma | |
| Hukku et al. | BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis | |
| Wang et al. | NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning | |
| Wang et al. | Cell division history encodes directional information of fate transitions | |
| Zhou et al. | Screening and comprehensive analysis of cancer-associated tRNA-derived fragments |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200825 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |