iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins
iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins
复制标题
iLoc-Animal：用于预测动物蛋白亚细胞定位的多标签学习分类器
DOI：
10.1039/c3mb25466f
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发表时间：
2013-01-01
影响因子：
--
通讯作者：
Chou, Kuo-Chen
中科院分区：
文献类型：
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作者：
Lin, Wei-Zhong;Fang, Jian-An;Chou, Kuo-Chen
预测蛋白质亚细胞定位是一个具有挑战性的问题，特别是当查询蛋白质具有多标记特征时，这意味着它们可能同时存在于两个或更多个不同的亚细胞定位位点处，或在两个或更多个不同的亚细胞定位位点之间移动。现有的方法大多只能用于处理单标记蛋白质。事实上，多标记蛋白是一个不容忽视的研究对象，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”方法，开发了一种名为iLoc-Animal的新预测器，可用于处理包含单标记和多标记动物（除人类外的后生动物）蛋白质的系统。同时，为了严格衡量多标签系统的预测质量，引入了五个指标：“绝对正确”，“绝对错误”（或汉明损失），“准确度”，“精度”和“召回”。作为证明，使用iLoc-Animal对分类为以下20个位置位点的动物蛋白质的基准数据集进行了折刀交叉验证：（1）顶体，（2）细胞膜，（3）中心粒，（4）中心体，（5）细胞皮层，（6）细胞质，（7）细胞骨架，（8）内质网，（9）核内体，（10）细胞外，（11）高尔基体，（12）溶酶体，（13）突触，（14）黑素体，（15）微粒体，（16）细胞核，（17）过氧化物酶体，（18）质膜，（19）纺锤体，（20）突触，其中许多蛋白质属于两个或多个位置。对于这样一个复杂的系统，iLoc-Animal在所有上述五个指数上取得的结果都非常令人鼓舞，表明预测器可能成为该领域的有用工具。我们注意到，多标记方法和严格的测量指标也可以用于研究分子生物学中的许多其他多标记问题。作为一个用户友好的网络服务器，iLoc-Animal可在网站http://www.jci-bioinfo.cn/iLoc-Animal上免费向公众开放。
Predicting protein subcellular localization is a challenging problem, particularly when query proteins have multi-label features meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multi-label proteins should not be ignored because they usually bear some special function worthy of in-depth studies. By introducing the "multi-label learning'' approach, a new predictor, called iLoc-Animal, has been developed that can be used to deal with the systems containing both single- and multi-label animal (metazoan except human) proteins. Meanwhile, to measure the prediction quality of a multi-label system in a rigorous way, five indices were introduced; they are "Absolute-True", "Absolute-False" (or Hamming-Loss"), "Accuracy", "Precision", and "Recall". As a demonstration, the jackknife cross-validation was performed with iLoc-Animal on a benchmark dataset of animal proteins classified into the following 20 location sites: (1) acrosome, (2) cell membrane, (3) centriole, (4) centrosome, (5) cell cortex, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracellular, (11) Golgi apparatus, (12) lysosome, (13) mitochondrion, (14) melanosome, (15) microsome, (16) nucleus, (17) peroxisome, (18) plasma membrane, (19) spindle, and (20) synapse, where many proteins belong to two or more locations. For such a complicated system, the outcomes achieved by iLoc-Animal for all the aforementioned five indices were quite encouraging, indicating that the predictor may become a useful tool in this area. It has not escaped our notice that the multi-label approach and the rigorous measurement metrics can also be used to investigate many other multi-label problems in molecular biology. As a user-friendly web-server, iLoc-Animal is freely accessible to the public at the web-site http://www.jci-bioinfo.cn/iLoc-Animal.