title: HybridRanker: Integrating network topology and biomedical knowledge to prioritize cancer candidate genes.
abstract: In the past few years, many researches have been conducted on identifying and prioritizing disease-related genes with the goal of achieving significant improvements in treatment and drug discovery. Both experimental and computational approaches have been exploited in recent studies to explore disease-susceptible genes. The experimental methods for identification of these genes are usually time-consuming and expensive. As a result, a substantial number of these studies have shown interest in utilizing computational techniques, commonly known as gene prioritization methods. From a conceptual point of view, these methods combine various sources of information about a particular disease of interest and then use it to discover and prioritize candidate disease genes. In this paper, we propose a gene prioritization method (HybridRanker), which exploits network topological features, as well as several biomedical data sources to identify candidate disease genes. In this approach, the genes are characterized using both local and global features of a protein-protein interaction (PPI) network. Furthermore, to obtain improved results for a particular disease of interest, HybridRanker incorporates data from diseases with similar symptoms and also from its comorbid diseases. We applied this new approach to identify and prioritize candidate disease genes of colorectal cancer (CRC) and the efficiency of HybridRanker was confirmed by leave-one-out cross-validation test. Moreover, in comparison with several well-known prioritization methods, HybridRanker shows higher performance in terms of different criteria.
authors.affiliation: Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran; School of Biological Sciences, Institute for Research in Foundation Sciences (IPM), Tehran, Iran. Electronic address: razzaghi@ut.ac.ir.
authors.forename: Zahra
authors.initials: Z
authors.lastname: Razaghi-Moghadam
authors.affiliation: Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: s.r_abdollahi@ut.ac.ir.
authors.forename: Razieh
authors.initials: R
authors.lastname: Abdollahi
authors.affiliation: University of Tehran, Tehran, Iran. Electronic address: sgoliaei@ut.ac.ir.
authors.forename: Sama
authors.initials: S
authors.lastname: Goliaei
authors.affiliation: Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: mo.ebrahimi@ut.ac.ir.
authors.forename: Morteza
authors.initials: M
authors.lastname: Ebrahimi
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030541:Databases, Genetic; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D060066:Protein Interaction Maps; D013223:Statistics as Topic
keywords: Colorectal cancer; Comorbidity; Gene prioritization; Protein-protein interaction network; Symptoms