Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine,biomedical text mining and network analysis |
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| Institution: | 1. MIT School of Bioengineering Science and Research, ADT University, Loni Kalbhor, Pune, 412201, India;2. Digital Information Resource Centre (DIRC) & Centre of Excellence in Scientific Computing (CoESC), CSIR-National Chemical Laboratory, Pune, 411008, India;3. Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008, India;1. Caisse nationale de l’assurance maladie des travailleurs salariés, direction de la stratégie des études et des statistiques, 26-50, avenue du Professeur-André-Lemierre, 75986 Paris cedex 20, France;2. Registre REIN, Agence de la biomédecine, 1, avenue du stade de France, 93212 Saint-Denis La Plaine cedex, France;1. Department of Pediatrics, DGSOM at UCLA, Los Angeles, California, United States;2. VA Greater Los Angeles Health Care System, Los Angeles, California, United States;3. Dumont-UCLA Transplant Center, Department of Surgery, DGSOM at UCLA, Los Angeles, California, United States;1. Centre for Nanobiotechnology, VIT University, Vellore 632014, Tamil Nadu, India;2. School of Advanced Sciences, VIT University, Vellore 632014, Tamil Nadu, India;1. Servicio de Cardiología, Hospital Universitario y Politécnico La Fe, Valencia, España;2. Inserm, U769, Université de Paris Sud, IFR141, LabEx Lermit, Châtenay-Malabry, Francia;3. Grupo de Investigación acreditado de Hemostasia, Trombosis, Arteriosclerosis y Biología Vascular, Instituto de Investigación Sanitaria La Fe, Valencia, España;4. Wales Heart Research Institute, Cardiff University School of Medicine, Cardiff, Reino Unido;5. Servicio de Histopatología, Instituto de Medicina Legal, Valencia, España;6. Servicio de Histopatología, Instituto Nacional de Toxicología y Ciencias Forenses, Madrid, España;1. School of Pharmaceutical Sciences, Shandong University, Jinan, China;2. Qilu Hospital, Shandong University, Jinan, China |
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| Abstract: | In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies. |
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| Keywords: | Diabetes SVM Protein-protein interactions Machine learning Protein interaction network |
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