title: iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.
abstract: Predicting membrane protein type is a challenging problem, particularly when the query proteins may simultaneously have two or more different types. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multiple-label proteins should not be ignored because they usually bear some special functions worthy of in-depth studies. By introducing the "multi-labeled learning" and hybridizing evolution information through Grey-PSSM, a novel predictor called iMem-Seq is developed that can be used to deal with the systems containing both single and multiple types of membrane proteins. As a demonstration, the jackknife cross-validation was performed with iMem-Seq on a benchmark dataset of membrane proteins classified into the eight types, where some proteins belong to two or there types, but none has ≥25% pairwise sequence identity to any other in a same subset. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a user-friendly web-server, iMem-Seq is freely accessible to the public at the website http://www.jci-bioinfo.cn/iMem-Seq .
authors: Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, 333046, China, jdzxiaoxuan@163.com.
authors: Xuan
authors: X
authors: Xiao
authors: Hong-Liang
authors: HL
authors: Zou
authors: Wei-Zhong
authors: WZ
authors: Lin
mesh_terms: D008565:Membrane Proteins / Q000737:chemistry / Q000145:classification* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D008565:Membrane Proteins