iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types
iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types
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DOI：
10.1007/s00232-015-9787-8
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Xuan Xiao;Hong-Liang Zou;Weizhong Lin
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文献类型：
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作者：
Xuan Xiao;Hong-Liang Zou;Weizhong Lin
预测膜蛋白类型是一个具有挑战性的问题，特别是当查询蛋白可能同时具有两种或多种不同类型时。现有的方法大多只能用于处理单标记蛋白质。事实上，多标记蛋白不应该被忽视，因为它们通常具有一些值得深入研究的特殊功能。通过引入“多标记学习”并通过 Grey-PSSM 杂交进化信息，开发了一种名为 iMem-Seq 的新型预测器，可用于处理包含单一和多种类型膜蛋白的系统。作为演示，使用 iMem-Seq 在分为八种类型的膜蛋白基准数据集上进行折刀交叉验证，其中一些蛋白质属于两种或多种类型，但没有一种与同一子集中的任何其他蛋白质具有 ≥25% 的成对序列同一性。通过严格的交叉验证证明，新的预测器明显优于所有同类预测器。作为一个用户友好的网络服务器，iMem-Seq 可以通过网站 http://www.jci-bioinfo.cn/iMem-Seq 免费向公众开放。
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 .