A Model-Based Method for Gene Dependency Measurement
Publication in refereed journal


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摘要Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method-DBoMM (Difference in BIC of Mixture Models)-for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall's tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, Arabidopsis thaliana data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is robust to noisy data. Of the 741 Escherichia coli regulatory interactions inferred by DBoMM at a 60% true positive rate, 65 are previously known interactions and 676 are novel predictions. To validate the new prediction, the promoter sequences of target genes regulated by the same transcription factors were analyzed and significant motifs were identified.
著者Zhang Q, Fan XD, Wang YJ, Sun MA, Sun SSM, Guo DJ
期刊名稱PLoS ONE
出版年份2012
月份7
日期19
卷號7
期次7
出版社PUBLIC LIBRARY SCIENCE
國際標準期刊號1932-6203
語言英式英語
Web of Science 學科類別Multidisciplinary Sciences; MULTIDISCIPLINARY SCIENCES; Science & Technology - Other Topics

上次更新時間 2020-20-09 於 02:50