title: Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes.
abstract: An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.
authors.affiliation: Bioinformatics Department of School of Basic Medical Sciences, Nanjing Medical University, Nanjing 210029, China ; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Nana
authors.initials: N
authors.lastname: Jin
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Deng
authors.initials: D
authors.lastname: Wu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Yonghui
authors.initials: Y
authors.lastname: Gong
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Xiaoman
authors.initials: X
authors.lastname: Bi
authors.affiliation: Bioinformatics Department of School of Basic Medical Sciences, Nanjing Medical University, Nanjing 210029, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Jiang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Kongning
authors.initials: K
authors.lastname: Li
authors.affiliation: Bioinformatics Department of School of Basic Medical Sciences, Nanjing Medical University, Nanjing 210029, China ; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Qianghu
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D004194:Disease / Q000235:genetics; D006801:Humans; D000069550:Machine Learning; D010641:Phenotype; D060066:Protein Interaction Maps; D012372:ROC Curve