Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms |
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Authors: | Wenyuan?Li Haiyan?Hu Yu?Huang Haifeng?Li Michael?R?Mehan Juan?Nunez-Iglesias Min?Xu Xifeng?Yan Email author" target="_blank">Xianghong?Jasmine?ZhouEmail author |
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Institution: | 1.Program in Computational Biology, Department of Biological Sciences,University of Southern California,Los Angeles,USA;2.School of Electrical Engineering and Computer Science,University of Central Florida,Orlando,USA;3.Motorola Labs,Tempe,USA;4.Computer Science Department,University of California at Santa Barbara,Santa Barbara,USA |
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Abstract: | The rapid accumulation of biological network data is creating an urgent need for computational methods capable of integrative
network analysis. This paper discusses a suite of algorithms that we have developed to discover biologically significant patterns
that appear frequently in multiple biological networks: coherent dense subgraphs, frequent dense vertex-sets, generic frequent
subgraphs, differential subgraphs, and recurrent heavy subgraphs. We demonstrate these methods on gene co-expression networks,
using the identified patterns to systematically annotate gene functions, map genome to phenome, and perform high-order cooperativity
analysis. |
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Keywords: | |
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