Network Analytics towards Drug Repositioning using Phosphorylated Proteins.
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Date
2019-01-01
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Department
Information Systems
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Information Systems
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Distribution Rights granted to UMBC by the author.
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Abstract
Drug Repositioning is an approach to discovering a new use of old drugs. However, current successes in drug repositioning have primarily been a result of serendipity or clinical observations, such as the observed use of sildenafil citrate (Viagra) mostly for the treatment of erectile dysfunction, but now repositioned for the treatment of pulmonary arterial hypertension, leprosy, and erectile dysfunction induced depression. Besides, thalidomide used for inducing sedation is now known to be potent for the treatment of multiple myeloma To transform this process systematically, many computational approaches have emerged with recent advances in computational technology, to automatically identify possible drug repositioning candidates by accessing an overwhelming volume of biomedical data. In this study, we described an effort made on computational drug repositioning by applying sequence encoding, sequence analysis, and network analytics against the phosphorylated proteins. We propose a novel framework towards computational drug repositioning with multiple components, 1) sequence analysis and sequence prioritization 2) biological interaction network construction by integrating heterogeneous interactions among gene, disease, protein, snp, etc.; 2) phosphorylated network creation 3) high-influence node detection/prioritization by applying network analysis and perturbation; 4) multimodal network creation and clustering 5) drug candidate identification and evaluation by using sequence analysis and cheminformatic techniques. We used the pim substrates, diabetes and rhabdomyosarcoma as a case study.