title: Matrix Factorization for Transcriptional Regulatory Network Inference.
abstract: Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis.
authors.affiliation: School of Medicine, Johns Hopkins University, Baltimore, MD 21205.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Ochs
authors.affiliation: School of Medicine, Johns Hopkins University, Baltimore, MD 21205.
authors.forename: Elana J
authors.initials: EJ
authors.lastname: Fertig
keywords: Bayesian statistics; Matrix factorization; NMF; Transcriptional Regulatory Network