A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation. Understanding the genetic background of diseases is crucial to medical research, with implications in diagnosis, treatment and drug development. As molecular approaches to this challenge are time consuming and costly, computational approaches offer an efficient alternative. Such approaches aim at prioritizing genes in a genomic interval of interest according to their predicted strength-of-association with a given disease. State-of-the-art prioritization problems are based on the observation that genes causing similar diseases tend to lie close to one another in a network of protein-protein interactions. Here we develop a novel prioritization approach that uses the network data in a global manner and can tie not only single genes but also whole protein machineries with a given disease. Our method, PRINCE, is shown to outperform previous methods in both the gene prioritization task and the protein complex task. Applying PRINCE to prostate cancer, alzheimer's disease and type 2 diabetes, we are able to infer new causal genes and related protein complexes with high confidence.

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During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.

Background: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. Results: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. Conclusion: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at http://www.cs.tau.ac.il/~rshamir/expander

Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework—Similarity-based Inference of drug-TARgets (SITAR)—for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline.com/cmb.