a semi-supervised method for predicting transcription factor–gene interactions in escherichia coli半监督方法预测转录因子基因在大肠杆菌的相互作用.pdfVIP

A Semi-Supervised Method for Predicting Transcription Factor–Gene Interactions in Escherichia coli 1 2¤ 2 ´ ´ 3 ´ 2 1 Jason Ernst , Qasim K. Beg , Krin A. Kay , Gabor Balazsi , Zoltan N. Oltvai , Ziv Bar-Joseph * 1 Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 2 Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 3 Department of Systems Biology, University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America Abstract While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi- supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor–gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor–gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcriptio