The Journal of Membrane Biology

, Volume 248, Issue 2, pp 179–186 | Cite as

A New Multi-label Classifier in Identifying the Functional Types of Human Membrane Proteins

Article
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Abstract

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their type. Given a membrane protein sequence, how can we identify its type(s)? Particularly, how can we deal with the multi-type problem since one membrane protein may simultaneously belong to two or more different types? To address these problems, which are obviously very important to both basic research and drug development, a new multi-label classifier was developed based on pseudo amino acid composition with multi-label k-nearest neighbor algorithm. The success rate achieved by the new predictor on the benchmark dataset by jackknife test is 73.94 %, indicating that the method is promising and the predictor may become a very useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying functional types of membrane proteins.

Keywords

Membrane proteins Pseudo amino acid composition ML-KNN algorithm Jackknife test 
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Supplementary material

232_2014_9755_MOESM1_ESM.pdf (9.6 mb)
Supplementary material 1 (PDF 9870 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Computer DepartmentJing-De-Zhen Ceramic InstituteJing-De-ZhenChina

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