title: Low-rank regularization for learning gene expression programs.
abstract: Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.
authors.affiliation: Department of Computer Science, University of California Irvine, Irvine, California, United States of America ; Department of Mathematics, University of California Irvine, Irvine, California, United States of America.
authors.forename: Guibo
authors.initials: G
authors.lastname: Ye
authors.affiliation: Department of Computer Science, University of California Irvine, Irvine, California, United States of America.
authors.forename: Mengfan
authors.initials: M
authors.lastname: Tang
authors.affiliation: Department of Mathematics, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Jian-Feng
authors.initials: JF
authors.lastname: Cai
authors.affiliation: Department of Mathematics, University of California Irvine, Irvine, California, United States of America ; Center for Complex Biological Systems, University of California Irvine, Irvine, California, United States of America.
authors.forename: Qing
authors.initials: Q
authors.lastname: Nie
authors.affiliation: Department of Computer Science, University of California Irvine, Irvine, California, United States of America ; Center for Complex Biological Systems, University of California Irvine, Irvine, California, United States of America.
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D015870:Gene Expression; D005786:Gene Expression Regulation; D006801:Humans; D008957:Models, Genetic; D012984:Software