Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Inferring Time-Varying Network Topologies from Gene Expression Data | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 51947 12 pages doi 2007 51947 Research Article Inferring Time-Varying Network Topologies from Gene Expression Data Arvind Rao 1 2 Alfred O. Hero III 1 2 David J. States 2 3 and James Douglas Engel1 4 1 Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2122 USA 2 Bioinformatics Graduate Program Center for Computational Medicine and Biology School of Medicine University of Michigan Ann Arbor MI 48109-2218 USA 3 Department of Human Genetics School of Medicine University of Michigan Ann Arbor MI 48109-0618 USA 4 Department of Cell and Developmental Biology School of Medicine University of Michigan Ann Arbor MI 48109-2200 USA Received 24 June 2006 Revised 4 December 2006 Accepted 17 February 2007 Recommended by Edward R. Dougherty Most current methods for gene regulatory network identification lead to the inference of steady-state networks that is networks prevalent over all times a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic that is time-varying fashion in order to account for different cellular states affecting the interactions amongst genes. In this work we present an approach regime-SSM to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics followed by system identification using a state-space model for each learnt cluster to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. Copyright 2007 Arvind Rao et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the .
