title: Driver Missense Mutation Identification Using Feature Selection and Model Fusion.
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.
authors.affiliation: 1 Department of Electrical and Computer Engineering, University of Miami , Coral Gables, Florida.
authors.forename: Ahmed T
authors.initials: AT
authors.lastname: Soliman
authors.affiliation: 1 Department of Electrical and Computer Engineering, University of Miami , Coral Gables, Florida.
authors.forename: Tao
authors.initials: T
authors.lastname: Meng
authors.affiliation: 2 School of Computing and Information Sciences, Florida International University , Miami, Florida.
authors.forename: Shu-Ching
authors.initials: SC
authors.lastname: Chen
authors.affiliation: 2 School of Computing and Information Sciences, Florida International University , Miami, Florida.
authors.forename: S S
authors.initials: SS
authors.lastname: Iyengar
authors.affiliation: 3 Department of Radiation Oncology, Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center , Dallas, Texas.
authors.forename: Puneeth
authors.initials: P
authors.lastname: Iyengar
authors.affiliation: 4 Anchorage & Valley Radiation Therapy Centers , Anchorage, Alaska.
authors.forename: John
authors.initials: J
authors.lastname: Yordy
authors.affiliation: 1 Department of Electrical and Computer Engineering, University of Miami , Coral Gables, Florida.
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Shyu
mesh_terms: D000818:Animals; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020125:Mutation, Missense; D009369:Neoplasms / Q000235:genetics*; D060388:Support Vector Machine
keywords: cancer genome; driver mutation; passenger mutation