a factor graph nested effects model to identify networks from genetic perturbations因子图模型嵌套的影响从遗传扰动识别网络.pdfVIP

A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations 1 2 2 2 3 2 Charles J. Vaske , Carrie House , Truong Luu , Bryan Frank , Chen-Hsiang Yeang *, Norman H. Lee *, Joshua M. Stuart1* 1 Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, California, United States of America, 2 Department of Pharmacology and Physiology, The George Washington University Medical Center, Washington, D.C., United States of America, 3 Institute of Statistical Science, Academia Sinica, Nankang, Taipei, Taiwan, Republic of China Abstract Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We app