Driver missense mutation identification using feature selection and model fusion

Ahmed T. Soliman, Tao Meng, Shu Ching Chen, S. S. Iyengar, Puneeth Iyengar, John Yordy, Mei-Ling Shyu

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Driver mutations propel oncogenesis and occur much less frequently than passenger mutations. The need for automatic and accurate identification of driver mutations has increased dramatically with the exponential growth of mutation data. Current computational solutions to identify driver mutations rely on sequence homology. Here we construct a machine learning-based framework that does not rely on sequence homology or domain knowledge to predict driver missense mutations. A windowing approach to represent the local environment of the sequence around the mutation point as a mutation sample is applied, followed by extraction of three sequence-level features from each sample. After selecting the most significant features, the support vector machine and multimodal fusion strategies are employed to give final predictions. The proposed framework achieves relatively high performance and outperforms current state-of-the-art algorithms. The ease of deploying the proposed framework and the relatively accurate performance make this solution applicable to large-scale mutation data analyses.

Original languageEnglish (US)
Pages (from-to)1075-1085
Number of pages11
JournalJournal of Computational Biology
Volume22
Issue number12
DOIs
StatePublished - Dec 1 2015

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Keywords

  • Cancer genome
  • Driver mutation
  • Passenger mutation

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Soliman, A. T., Meng, T., Chen, S. C., Iyengar, S. S., Iyengar, P., Yordy, J., & Shyu, M-L. (2015). Driver missense mutation identification using feature selection and model fusion. Journal of Computational Biology, 22(12), 1075-1085. https://doi.org/10.1089/cmb.2015.0110