Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms
| Autor: | Yu S. Huang, Xianghong Jasmine Zhou, Wenyuan Li, Haiyan Hu, Michael R. Mehan, Haifeng Li, Xifeng Yan, Min Xu, Juan Nunez-Iglesias |
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| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
0303 health sciences Phenome computer.software_genre Biochemistry Genetics and Molecular Biology (miscellaneous) Genome 03 medical and health sciences 0302 clinical medicine Data mining Algorithm computer 030217 neurology & neurosurgery Biological network 030304 developmental biology Network analysis Mathematics |
| Zdroj: | Statistics in Biosciences. 4:157-176 |
| ISSN: | 1867-1772 1867-1764 |
| DOI: | 10.1007/s12561-011-9047-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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