A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.

ABSTRACT: Cancerlectins have an inhibitory effect on the growth of cancer cells and are currently being employed as therapeutic agents. The accurate identification of the cancerlectins should provide insight into the molecular mechanisms of cancers. In this study, a new computational method based on the RF (Random Forest) algorithm is proposed for further improving the performance of identifying cancerlectins. Hybrid feature space before feature selection is developed by combining different individual feature spaces, CTD (Composition, Transition, and Distribution), PseAAC (Pseudo Amino Acid Composition), PSSM (Position-Specific Scoring Matrix), and disorder. The SMOTE (Synthetic Minority Oversampling Technique) is applied to solve the imbalanced data problem. To reduce feature redundancy and computa

SUBMITTER: Yang R 

PROVIDER: S-EPMC5820548 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature