Matrix factorisation methods applied in microarray data analysis
International Journal of Data Mining and Bioinformatics
Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks
International Journal of Data Mining and Bioinformatics
A Weighted Principal Component Analysis and Its Application to Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Uncovering transcriptional regulatory networks by sparse Bayesian factor model
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Noniterative Convex Optimization Methods for Network Component Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Microarray gene expression and cross-linking chromatin immunoprecipitation data contain voluminous information that can help the identification of transcriptional regulatory networks at the full genome scale. Such high-throughput data are noisy however. In contrast, from the biomedical literature, we can find many evidenced transcription factor (TF)--target gene binding relationships that have been elucidated at the molecular level. But such sporadically generated knowledge only offers glimpses on limited patches of the network. How to incorporate this valuable knowledge resource to build more reliable network models remains a question. Results: We present a modified factor analysis approach. Our algorithm starts with the evidenced TF--gene linkages. It iterates between the network configuration estimation step and the connection strength estimation step, using the high-throughput data, till convergence. We report two comprehensive regulatory networks obtained for Saccharomyces cerevisiae, one under the normal growth condition and the other under the environmental stress condition. Contact: kcli@stat.ucla.edu Supplementary information: http://kiefer.stat.ucla.edu/lap2/download/bti656_supplement.pdf