title: Identifying the singleplex and multiplex proteins based on transductive learning for protein subcellular localization prediction.
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.
authors.affiliation: School of Control Science and Engineering, Dalian University of Technology, Dalian, People's Republic of China.
authors.forename: Junzhe
authors.initials: J
authors.lastname: Cao
authors.forename: Wenqi
authors.initials: W
authors.lastname: Liu
authors.forename: Jianjun
authors.initials: J
authors.lastname: He
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D011485:Protein Binding; D055503:Protein Multimerization; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D011506:Proteins