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Indexed by:期刊论文

Date of Publication:2013-07-01

Journal:BIOTECHNOLOGY LETTERS

Included Journals:SCIE、EI、PubMed

Volume:35

Issue:7

Page Number:1107-1113

ISSN No.:0141-5492

Key Words:Independent data set test; Multiplex protein; Protein subcellular location prediction; Singleplex protein; Transductive learning; Weighted neighborhood graph

Abstract:A new method is proposed to identify whether a query protein is singleplex or multiplex for improving the quality of protein subcellular localization prediction. Based on the transductive learning technique, this approach utilizes the information from the both query proteins and known proteins to estimate the subcellular location number of every query protein so that the singleplex and multiplex proteins can be recognized and distinguished. Each query protein is then dealt with by a targeted single-label or multi-label predictor to achieve a high-accuracy prediction result. We assess the performance of the proposed approach by applying it to three groups of protein sequences datasets. Simulation experiments show that the proposed approach can effectively identify the singleplex and multiplex proteins. Through a comparison, the reliably of this method for enhancing the power of predicting protein subcellular localization can also be verified.