title: Integration of multiple biological features yields high confidence human protein interactome.
abstract: The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.
authors.affiliation: Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey. Electronic address: kubra.karagoz@marun.edu.tr.
authors.forename: Kubra
authors.initials: K
authors.lastname: Karagoz
authors.affiliation: Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey. Electronic address: tubasevim@hotmail.com.
authors.forename: Tuba
authors.initials: T
authors.lastname: Sevimoglu
authors.affiliation: Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey. Electronic address: kazim.arga@marmara.edu.tr.
authors.forename: Kazim Yalcin
authors.initials: KY
authors.lastname: Arga
mesh_terms: D016001:Confidence Intervals; D003625:Data Collection; D030562:Databases, Protein; D006801:Humans; D009369:Neoplasms / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D012372:ROC Curve; D015203:Reproducibility of Results; D015398:Signal Transduction
keywords: Cancer subnetworks; Network topology; Protein interaction network; Protein–protein interaction