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论文类型： 期刊论文

发表时间： 2016-04-01

发表刊物： MATHEMATICAL BIOSCIENCES

收录刊物： SCIE、EI、PubMed、Scopus

卷号： 274

页面范围： 25-32

ISSN号： 0025-5564

关键字： Bi-relational graph; Transductive learning; Multi-label classification; PPI; Classifier integration

摘要： One of the challenging tasks of bioinformatics is to predict more accurate and confident protein functions from genomics and proteomics datasets. Computational approaches use a variety of high throughput experimental data, such as protein-protein interaction (PPI), protein sequences and phylogenetic profiles, to predict protein functions. This paper presents a method that uses transductive multi-label learning algorithm by integrating multiple data sources for classification. Multiple proteomics datasets are integrated to make inferences about functions of unknown proteins and use a directed bi-relational graph to assign labels to unannotated proteins. Our method, bi-relational graph based transductive multi-label function annotation (Bi-TMF) uses functional correlation and topological PPI network properties on both the training and testing datasets to predict protein functions through data fusion of the individual kernel result. The main purpose of our proposed method is to enhance the performance of classifier integration for protein function prediction algorithms. Experimental results demonstrate the effectiveness and efficiency of Bi-TMF on multi-sources datasets in yeast, human and mouse benchmarks. Bi-TMF outperforms other recently proposed methods. (C) 2016 Elsevier Inc. All rights reserved.