Computational Studies for Medical Profiling and Algorithmic Designing of Anti-Hepatitis Peptides | Shiv Nadar University
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Computational Studies for Medical Profiling and Algorithmic Designing of Anti-Hepatitis Peptides

Recent increase in hepatitis infection and the emergence of resistant strains have spurred the research on hepatitis diagnostics and therapeutics. By amalgamation of various techniques of advanced computational, mathematical and statistical analysis, the pattern mining based prediction studies are being undertaken for understanding the biological or medical data. The need of the hour is to study patterns which can lead to improved understanding with respect to genotypic variation of the virus, inhibitory antiviral drug activity and clinical outcome.

This thesis addresses three major aspects of the aforementioned problem: (1) phylogenetic study (2) prediction of compound activity based on the available physicochemical data and (3) predicting medical outcomes. While we performed analysis of the available Hepatitis medical dataset for finding improved event prediction model, we also undertook phylogenetic analysis of hepatitis E virus using alignment free methods. As a promising theranostic, Anti-hepatitis peptides were collected, analyzed for modeling and activity relationship and compiled as a database cum predictive web-server. We hypothesize that the analysis based on informative predictors will aid in improving the input for next level analysis and foster effective and efficient patient care. 

Department: 
Life Sciences
Year: 
2017
Student Name: 
Gunjan Mishra
Faculty Advisor: 
Co-Faculty Advisor: 

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