Name
Title | ||
|---|---|---|
Statistical identification of gene association by CID in application of constructing ER regulatory network. |
Abstract | ||
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Background: A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor (ER) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). Results: The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1186/1471-2105-10-85 | BMC Bioinformatics |
Keywords | Field | DocType |
system biology,bioinformatics,coefficient of determination,transcription factors,linear model,high throughput,estrogen receptor,conditional distribution,transcription factor,breast cancer,messenger rna,genes,statistical analysis,gene regulatory network,microarrays,gene expression,algorithms,mutual information | Conditional probability distribution,Biology,Categorical variable,Systems biology,Estrogen receptor alpha,Bioinformatics,Gene regulatory network,Genetics,DNA microarray,Gene expression profiling,In silico | Journal |
Volume | Issue | ISSN |
10 | 1 | 1471-2105 |
Citations | PageRank | References |
18 | 0.57 | 8 |
Authors | ||
14 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Yu D. Liu | 1 | 19 | 1.32 |
| Chien-Yu Chen | 2 | 367 | 29.24 |
| Mei-Ju May Chen | 3 | 23 | 1.05 |
| Ming-Shian Tsai | 4 | 54 | 1.77 |
| Cho-Han S. Lee | 5 | 18 | 0.57 |
| Tzu L. Phang | 6 | 28 | 1.48 |
| Li-Yun Chang | 7 | 18 | 0.91 |
| Wen-hung Kuo | 8 | 456 | 27.00 |
| Hsiao-Lin Hwa | 9 | 21 | 1.65 |
| Huang-Chun Lien | 10 | 18 | 0.57 |
| Shih-Ming Jung | 11 | 18 | 0.57 |
| Yi-Shing Lin | 12 | 18 | 0.57 |
| K.-J. Chang | 13 | 33 | 3.98 |
| Fon-Jou Hsieh | 14 | 18 | 0.91 |