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.

Acknowledgments:I would like to thank my thesis advisor, Prof. Roded Sharan for his guidance, ideas, advices, the constructive criticism and dedication. I would like to thank Osnat Atias, my wife, for her unwavering support, backup and understanding. She is ever a constant source of motivation for me. AbstractOne of the critical stages in drug development is the identification of potential side effects for promising drug leads. Large scale clinical experiments aimed at discovering such side effects are very costly and may miss subtle or rare side effects. To date, and to the best of our knowledge, no computational approach was suggested to systematically tackle this challenge. In this thesis we report on a novel approach to predict the side effects of a given drug. Starting from a query drug, a combination of canonical correlation analysis and network-based diffusion are applied to predict its side effects. Both methods are trained using data on known drug-side effect associations; they exploit molecular data on the drugs, such as their chemical structure or the response of different cell lines to treatment with the drugs.We evaluate our method by measuring its performance in cross validation using a comprehensive data set of 692 drugs and their known side effects derived from package inserts.For 35% of the drugs the top scoring side effect matches a known side effect of the drug; for almost two thirds of the drugs our method infers a correct side effect among the five top ranking predictions. To further validate our prediction scheme, we apply it in a blind test to about 450 drugs that were not part of the annotated data, but for which some side effect information exists in a large scale database. Remarkably, even on these unseen data, our method is able to infer side effects that highly match existing knowledge: for 45% of the drugs, a correct side effect is included among the five top ranking predictions. As a final validation and a potential additional application of our method, we show its utility in drug target elucidation. Specifically, we use our method to predict the side effects of more than 4,000 drugs, and find a significant correlation between side effect similarity and target similarity among these drugs. While such correlation was previously observed and successfully used to predict drug targets, it was limited in scope to drugs with known side effects. Our method thus represents a promising first step toward shortcutting the process and reducing the cost of side effect elucidation.

The initial step in microRNA (miRNA) biogenesis requires processing of the precursor miRNA (pre-miRNA) from a longer primary transcript. Many pre-miRNAs originate from introns, and both a mature miRNA and a spliced RNA can be generated from the same transcription unit. We have identified a mechanism in which RNA splicing negatively regulates the processing of pre-miRNAs that overlap exon-intron junctions. Computational analysis identified dozens of such pre-miRNAs, and experimental validation demonstrated competitive interaction between the Microprocessor complex and the splicing machinery. Tissue-specific alternative splicing regulates maturation of one such miRNA, miR-412, resulting in effects on its targets that code a protein network involved in neuronal cell death processes. This mode of regulation specifically controls maturation of splice-site-overlapping pre-miRNAs but not pre-miRNAs located completely within introns or exons of the same transcript. Our data present a biological role of alternative splicing in regulation of miRNA biogenesis.

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We currently lack a broader mechanistic understanding of the integration of the early secretory pathway with other homeostatic processes such as cell growth. Here, we explore the possibility that Sec16A, a major constituent of endoplasmic reticulum exit sites (ERES), acts as an integrator of growth factor signaling. Surprisingly, we find that Sec16A is a short-lived protein that is regulated by growth factors in a manner dependent on Egr family transcription factors. We hypothesize that Sec16A acts as a central node in a coherent feed-forward loop that detects persistent growth factor stimuli to increase ERES number. Consistent with this notion, Sec16A is also regulated by short-term growth factor treatment that leads to increased turnover of Sec16A at ERES. Finally, we demonstrate that Sec16A depletion reduces proliferation, whereas its overexpression increases proliferation. Together with our finding that growth factors regulate Sec16A levels and its dynamics on ERES, we propose that this protein acts as an integrator linking growth factor signaling and secretion. This provides a mechanistic basis for the previously proposed link between secretion and proliferation.