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Algorithms for detecting protein complexes in PPI networks: an evaluation study

Monash University
Chee-Keong Kwoh (Aggregated by) Min Wu (Aggregated by) Xiaoli Li (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/03/5a137247533fb&rft.title=Algorithms for detecting protein complexes in PPI networks: an evaluation study&rft.identifier=https://doi.org/10.4225/03/5a137247533fb&rft.publisher=Monash University&rft.description=Since protein complexes play important biological roles in cells, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) data. In this paper, we first review four reputed protein-complex detection algorithms (MCODE[2], MCL[21], CPA[1] and DECAFF[14]) and then present a comprehensive evaluation among them on two popular yeast PPI data3. We also discuss their relative strengthes and disadvantages to guide interested researchers. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.&rft.creator=Chee-Keong Kwoh&rft.creator=Min Wu&rft.creator=Xiaoli Li&rft.date=2017&rft_rights=In Copyright&rft_subject=Bioinformatics -- Congresses&rft_subject=Computational biology -- Congresses&rft_subject=Computer vision in medicine -- Congresses&rft_subject=Computational biology -- Methods -- Congresses&rft_subject=Pattern recognition, automated -- Methods -- Congresses&rft_subject=2008&rft_subject=conference paper&rft_subject=1959.1/63703&rft_subject=monash:7859&rft_subject=Bioinformatics&rft_subject=Pattern Recognition and Data Mining&rft_subject=Bioinformatics Software&rft.type=dataset&rft.language=English Access the data
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Since protein complexes play important biological roles in cells, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) data. In this paper, we first review four reputed protein-complex detection algorithms (MCODE[2], MCL[21], CPA[1] and DECAFF[14]) and then present a comprehensive evaluation among them on two popular yeast PPI data3. We also discuss their relative strengthes and disadvantages to guide interested researchers. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Issued: 2017-11-21

Created: 2017-11-21

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1959.1/63703 | 2008 | Bioinformatics | Bioinformatics -- Congresses | Bioinformatics Software | Computational biology -- Congresses | Computational biology -- Methods -- Congresses | Computer vision in medicine -- Congresses | Pattern Recognition and Data Mining | Pattern recognition, automated -- Methods -- Congresses | conference paper | monash:7859 |

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ACN 633 798 857