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      HOGMMNC: a higher order graph matching with multiple network constraints model for gene–drug regulatory modules identification

      1 , 2 , 1 , 1 , 1 , 2 , 1
      Bioinformatics
      Oxford University Press (OUP)

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          Abstract

          The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene-drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug action, but is also important in the development of effective treatments for patients. However, accurately determining the complex associations among pharmacogenomic data remains challenging. We propose a higher order graph matching with multiple network constraints (HOGMMNC) model to accurately identify gene-drug modules. The HOGMMNC model aims to capture the inherent structural relations within data drawn from multiple sources by hypergraph matching. The proposed technique seamlessly integrates prior constraints to enhance the accuracy and reliability of the identified relations. An effective numerical solution is combined with a novel sampling strategy to solve the problem efficiently.

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          Most cited references31

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          Is Open Access

          A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

          The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.
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            Protein localization in disease and therapy.

            The eukaryotic cell is organized into membrane-covered compartments that are characterized by specific sets of proteins and biochemically distinct cellular processes. The appropriate subcellular localization of proteins is crucial because it provides the physiological context for their function. In this Commentary, we give a brief overview of the different mechanisms that are involved in protein trafficking and describe how aberrant localization of proteins contributes to the pathogenesis of many human diseases, such as metabolic, cardiovascular and neurodegenerative diseases, as well as cancer. Accordingly, modifying the disease-related subcellular mislocalization of proteins might be an attractive means of therapeutic intervention. In particular, cellular processes that link protein folding and cell signaling, as well as nuclear import and export, to the subcellular localization of proteins have been proposed as targets for therapeutic intervention. We discuss the concepts involved in the therapeutic restoration of disrupted physiological protein localization and therapeutic mislocalization as a strategy to inactivate disease-causing proteins.
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              Is Open Access

              Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

              Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ‘omic’ data.
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                Author and article information

                Journal
                Bioinformatics
                Oxford University Press (OUP)
                1367-4803
                1460-2059
                February 15 2019
                February 15 2019
                July 24 2018
                February 15 2019
                February 15 2019
                July 24 2018
                : 35
                : 4
                : 602-610
                Affiliations
                [1 ]School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
                [2 ]Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, South China University of Technology, Guangzhou, China
                Article
                10.1093/bioinformatics/bty662
                30052773
                65f240be-9f5c-4cd0-9297-871fb565865a
                © 2018

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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