title: Identification and analysis of driver missense mutations using rotation forest with feature selection.
abstract: Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes. Many approaches are proposed which use supervised machine learning techniques for prediction with features obtained by some databases. However, often we do not know which features are important for driver mutations prediction. In this study, we propose a novel feature selection method (called DX) from 126 candidate features' set. In order to obtain the best performance, rotation forest algorithm was adopted to perform the experiment. On the train dataset which was collected from COSMIC and Swiss-Prot databases, we are able to obtain high prediction performance with 88.03% accuracy, 93.9% precision, and 81.35% recall when the 11 top-ranked features were used. Comparison with other various techniques in the TP53, EGFR, and Cosmic2plus datasets shows the generality of our method.
authors.affiliation: Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, Anhui 230601, China ; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Xiuquan
authors.identifier: 0000-0001-7913-7605
authors.initials: X
authors.lastname: Du
authors.affiliation: Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, Anhui 230601, China ; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Jiaxing
authors.initials: J
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001483:Base Sequence; D014408:Biomarkers, Tumor / Q000235:genetics*; D003198:Computer Simulation; D004252:DNA Mutational Analysis / Q000379:methods*; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020125:Mutation, Missense / Q000235:genetics*; D009363:Neoplasm Proteins / Q000235:genetics*; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers; D009363:Neoplasm Proteins