Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach |
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| Authors: | Wenhui Wang Juan Nunez-Iglesias Yihui Luan and Fengzhu Sun |
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| Institution: | (1) School of Mathematics, Shandong University, Jinan, Shandong, 250100, PR China;(2) Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA 90089-2910, USA;(3) MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, PR China |
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| Abstract: | Background Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic
networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer
biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not
clear whether such complex models are better suited to find biologically meaningful subnetworks. |
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