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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction
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Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction

机译：跨导学习的多标签蛋白亚叶绿体定位预测

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摘要

Predicting the localization of chloroplast proteins at the sub-subcellular level is an essential yet challenging step to elucidate their functions. Most of the existing subchloroplast localization predictors are limited to predicting single-location proteins and ignore the multi-location chloroplast proteins. While recent studies have led to some multi-location chloroplast predictors, they usually perform poorly. This paper proposes an ensemble transductive learning method to tackle this multi-label classification problem. Specifically, given a protein in a dataset, its composition-based sequence information and profile-based evolutionary information are respectively extracted. These two kinds of features are respectively compared with those of other proteins in the dataset. The comparisons lead to two similarity vectors which are weighted-combined to constitute an ensemble feature vector. A transductive learning model based on the least squares and nearest neighbor algorithms is proposed to process the ensemble features. We refer to the resulting predictor as as EnTrans-Chlo. Experimental results on a stringent benchmark dataset and a novel dataset demonstrate that EnTrans-Chlo significantly outperforms state-of-the-art predictors and particularly gains more than 4 percent (absolute) improvement on the overall actual accuracy. For readers’ convenience, EnTrans-Chlo is freely available online at http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.
机译：预测叶绿体蛋白在亚亚细胞水平的定位是阐明其功能的重要但具有挑战性的步骤。大多数现有的亚叶绿体定位预测器仅限于预测单位置蛋白，而忽略了多位置叶绿体蛋白。尽管最近的研究导致了一些多位置叶绿体预测因子，但它们通常表现不佳。本文提出了一种集成的转导学习方法来解决这个多标签分类问题。具体地，给定数据集中的蛋白质，分别提取其基于组成的序列信息和基于概况的进化信息。将这两种特征分别与数据集中其他蛋白质的特征进行比较。比较产生两个相似度向量，将它们加权组合以构成整体特征向量。提出了一种基于最小二乘和最近邻算法的跨语言学习模型来处理集合特征。我们将结果预测变量称为EnTrans-Chlo。在严格的基准数据集和新颖的数据集上的实验结果表明，EnTrans-Chlo大大优于最新的预测指标，尤其是整体实际精度提高了4％以上（绝对）。为了方便读者，EnTrans-Chlo可从http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/免费在线获得。

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