iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types

iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types 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 . The Journal of Membrane Biology Springer Journals

iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types

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Springer US
Copyright © 2015 by Springer Science+Business Media New York
Life Sciences; Biochemistry, general; Human Physiology
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