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      A Graphic Method for Identification of Novel Glioma Related Genes

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          Abstract

          Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.

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          Most cited references56

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          CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009.

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            Wnt signaling and cancer.

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              Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

              Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Availability: Softwares are available upon request. Contact: Yoshihiro.Yamanishi@ensmp.fr Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
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                Author and article information

                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi Publishing Corporation
                2314-6133
                2314-6141
                2014
                23 June 2014
                : 2014
                : 891945
                Affiliations
                1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
                2State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China
                3Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun 130033, China
                4Institute of Systems Biology, Shanghai University, Shanghai 200444, China
                5CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
                Author notes

                Academic Editor: Tao Huang

                Author information
                http://orcid.org/0000-0002-8304-1531
                http://orcid.org/0000-0002-1901-9778
                Article
                10.1155/2014/891945
                4094879
                25050377
                c7c42f2f-5b71-47be-a7e7-3efe8eaec01e
                Copyright © 2014 Yu-Fei Gao et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 April 2014
                : 25 May 2014
                : 28 May 2014
                Categories
                Research Article

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