Issue 3, 2015

Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure

Abstract

Membrane transporters play crucial roles in the fundamental cellular processes of living organisms. Computational techniques are very necessary to annotate the transporter functions. In this study, a multi-class K nearest neighbor classifier based on the increment of diversity (KNN-ID) was developed to discriminate the membrane transporter types when the increment of diversity (ID) was introduced as one of the novel similarity distances. Comparisons with multiple recently published methods showed that the proposed KNN-ID method outperformed the other methods, obtaining more than 20% improvement for overall accuracy. The overall prediction accuracy reached was 83.1%, when the K was selected as 2. The prediction sensitivity achieved 76.7%, 89.1%, 80.1% for channels/pores, electrochemical potential-driven transporters, primary active transporters, respectively. Discrimination and comparison between any two different classes of transporters further demonstrated that the proposed method is a potential classifier and will play a complementary role for facilitating the functional assignment of transporters.

Graphical abstract: Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure

Article information

Article type
Paper
Submitted
24 Nov 2014
Accepted
06 Jan 2015
First published
06 Jan 2015

Mol. BioSyst., 2015,11, 950-957

Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure

Y. Zuo, W. Su, S. Zhang, S. Wang, C. Wu, L. Yang and G. Li, Mol. BioSyst., 2015, 11, 950 DOI: 10.1039/C4MB00681J

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