Submit Manuscript  

Article Details


TargetMM: accurate missense mutation prediction by utilizing local and global sequence information with classifier ensemble

Author(s):

Fang Ge, Jun Hu, Yi-Heng Zhu, Muhammad Arif and Dong-Jun Yu*   Pages 1 - 17 ( 17 )

Abstract:


Aim and Objective: Missense mutation (MM) may lead to various human diseases by disabling proteins. Accurate prediction of MM is important and challenging for both protein function annotation and drug design. Although several computational methods yielded acceptable success rates, there is still room for further enhancing the prediction performance of MM.

Materials and Methods: In the present study, we designed a new feature extracting method, which considers the impact degree of residues in the microenvironment range to the mutation site. Stringent cross-validation and independent test on benchmark datasets were performed to evaluate the efficacy of the proposed feature extracting method. Furthermore, three heterogeneous prediction models were trained and then ensembled for the final prediction.

Results: By combining the feature representation method and classifier ensemble technique, we reported a novel MM predictor called TargetMM for identifying the pathogenic mutations from the neutral ones.

Conclusion: Comparison outcomes based on statistical evaluation demonstrate that TargetMM outperforms the prior advanced methods on the independent test data. The source code and benchmark datasets of TargetMM are freely available at https://github.com/sera616/TargetMM.git for academic use.

Keywords:

Human disease, missense mutation, mutation prediction, feature extracting, classifier ensemble

Affiliation:

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094



Read Full-Text article