Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Novel metrics for evaluating the functional coherence of protein groups via protein semantic network. | Open Access Metlfccl Novel metrics for evaluating the functional coherence of protein groups via protein semantic network Bin Zheng and Xinghua Lu Addresses Department of Biostatistics Bioinformatics and Epidemiology 135 Cannon Street Charleston South Carolina 29425 USA. Taboratory for Functional Neurogenomics Center for Neurologic Diseases Harvard Medical School and Brigham and Women s Hospital Landsdowne Street Cambridge Massachusetts 02139 USA. Correspondence Xinghua Lu. Email lux@ Published 31 July 2007 Genome Biology 2007 8 R153 doi gb-2007-8-7-r153 The electronic version of this article is the complete one and can be found online at http 2007 8 7 R153 Received 4 January 2007 Revised 23 April 2007 Accepted 31 July 2007 2007 Zheng and Lu licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract We present the metrics for assessing overall functional coherence of a group of proteins based on associated biomedical literature. A probabilistic topic model is applied to extract biologic concepts from a corpus of protein-related biomedical literature. Bipartite protein semantic networks are constructed so that the functional coherence of a protein group can be evaluated with metrics that measure the closeness and strength of connectivity of the proteins in the network. Background A cellular function is usually carried out by a group of proteins such as the proteins that participate in a common metabolic pathway or a signal transduction pathway. Based on the assumption that the expression of the proteins involved in a biologic process should be coordinated many computational methods have been developed to identify the potential modules of genes or proteins based on high throughput technologies
