MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier
MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier
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MultiP-SChlo：利用 Chou 伪氨基酸组成和新型多标签分类器进行多标签蛋白质叶绿体定位预测
DOI：
10.1093/bioinformatics/btv212
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发表时间：
2015-08-15
期刊：
影响因子：
5.8
通讯作者：
Li, Guo-Zheng
中科院分区：
文献类型：
--
作者：
Wang, Xiao;Zhang, Weiwei;Li, Guo-Zheng
动机：蛋白质在叶绿体细胞器中的亚叶绿体定位有助于了解叶绿体蛋白质的功能。蛋白质亚叶绿体定位的计算预测方法较少。然而，这些已有的工作在构建预测模型时忽略了具有多个亚叶绿体位置的蛋白质，使得它们只能预测这类多标记蛋白质的所有亚叶绿体位置中的一个。为了解决这个问题，通过同时利用标签特定特征和标签相关性，开发了一种新的多标记分类器，用于预测具有单个和多个定位位点的蛋白质亚叶绿体定位。作为初步研究，我们提出的算法的整体准确率达到55.52%，这是相当高的，能够成为一个有前途的工具，为进一步的研究。
Motivation: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins.Results: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies.