Conference proceeding
Consistently Predicting Protein Function Based on MKL
2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, PROCEEDINGS, pp.219-222
IEEE International Conference on Bioinformatics and Biomedicine Workshop-BIBMW
2008 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Philadelphia, Pennsylvania, United States, 03 Nov 2008 - 05 Nov 2008)
Nov 2008
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Using Multiple Kernels Learning(MKL) to integrate heterogeneous data sources to train Support Vector Machine(SVM) classifier is becoming popular For the protein function prediction problem, all the function categories form a directed acyclic graph(DAG), that is, Gene Ontology(GO). Given a protein to be predicted, after applying a trained SVM to output probabilistic prediction at each function category node, we use a cost-based model to consistently adjust function assignment on GO, which is called PredConsist/MKL. Experiments show PredConsist/MKL has higher ROC score than unadjusted method SDP/SVM, and better prediction performance. This adjustment is necessary.
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Details
- Title
- Consistently Predicting Protein Function Based on MKL
- Creators
- Yiming Chen - National University of Defense TechnologyZhoujun Li - Beihang UniversityXiaohua Hu - Drexel University, Information Science (Informatics)Junwan Liu - National University of Defense Technology
- Publication Details
- 2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, PROCEEDINGS, pp.219-222
- Conference
- 2008 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Philadelphia, Pennsylvania, United States, 03 Nov 2008 - 05 Nov 2008)
- Series
- IEEE International Conference on Bioinformatics and Biomedicine Workshop-BIBMW
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- 60573057 / National Scientific Foundations in China 06YJ16 / Hunan Agricultural University, China
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019167322304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Biomedical