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Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins (Predict Functional Linkages with a Hetero Method)

Lin, Tzu-Wen ; Wu, Jian-Wei ; Chang, Darby Tien-Hao ;Fraternali, Franca (Editor)

2013, Vol.8(9), p.e75940 [Peer Reviewed Journal]

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  • Title:
    Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins (Predict Functional Linkages with a Hetero Method)
  • Author/Creator: Lin, Tzu-Wen ; Wu, Jian-Wei ; Chang, Darby Tien-Hao
  • Fraternali, Franca (Editor)
  • Language: English
  • Subjects: Research Article
  • Is Part Of: 2013, Vol.8(9), p.e75940
  • Description: Annotating protein functions and linking proteins with similar functions are important in systems biology. The rapid growth rate of newly sequenced genomes calls for the development of computational methods to help experimental techniques. Phylogenetic profiling (PP) is a method that exploits the evolutionary co-occurrence pattern to identify functional related proteins. However, PP-based methods delivered satisfactory performance only on prokaryotes but not on eukaryotes. This study proposed a two-stage framework to predict protein functional linkages, which successfully enhances a PP-based method with machine learning. The experimental results show that the proposed two-stage framework achieved the best overall performance in comparison with three PP-based methods.
  • Identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0075940