Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
IEEE Trans Nanobioscience. 2012 Sep;11(3):237-43. doi: 10.1109/TNB.2012.2212249.
Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multilabel learning problem. We present and compare two multilabel learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multilabel learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.
蛋白质亚细胞定位旨在使用计算方法预测蛋白质在细胞内的位置。蛋白质亚细胞定位的知识表明蛋白质的功能，并有助于识别药物靶点。蛋白质亚细胞定位的预测是一个重要但具有挑战性的问题，特别是当蛋白质可能同时存在于两个或更多不同的亚细胞位置或在它们之间移动时。大多数现有的蛋白质亚细胞定位方法仅用于处理单定位蛋白质。为了更好地反映多聚蛋白质的特征，我们将多聚蛋白质的亚细胞定位预测表述为多标签学习问题。我们提出并比较了两种多标签学习方法，分别利用标签之间的相关性和利用标签特定的特征来诱导高质量的预测模型。在不同生物体的六个蛋白质数据集上的实验结果表明，我们描述的方法比任何现有方法都具有显著更高的性能。在不同的多标签学习方法中，我们发现利用标签相关性的方法比利用标签特定特征的方法表现更好。
