Prediction of Dengue-Human Protein Interaction Using Artificial Neural Network for Anti-Viral Drug Discovery

Dengue fever is a viral disease transmitted by Aedesaegyti mosquito species. Every year more than 2 million people are getting affected by this disease. Efforts are taken globally to develop a vaccine against dengue. Dengue-Human Protein interaction plays a vital role in disease prediction. Due to the recent advancements in computational biology, effective application of algorithms to analyze Protein-Protein Interaction will get a better insight into the disease. DenvInt is a biological repository which provides detailed information on Protein-Protein Interaction between dengue-human and dengue-mosquito. The main objective of this study is to computationally predict the dengue-human protein interaction that assists to know which human proteins are affected when dengue virus enters into the human body. This study focuses on applying Artificial Neural Network mechanism for predicting dengue human protein interaction. The results show the effectiveness of the features and algorithm in predicting the interaction type which leads to the development of anti-viral drugs.

Keywords: DenvInt, Rank Correlation, MIN MAX Normalization, Feed Forward Back Propagation, Artificial Neural Network, Dengue Drug Discovery

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Jainul Fathima, A. and Rathinasamy, Revathy and Balamurali, S. and Murugaboopathi, G., Prediction of Dengue-Human Protein Interaction Using Artificial Neural Network for Anti-Viral Drug Discovery (February 24, 2019). Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur - India, February 26-28, 2019, Available at SSRN: https://ssrn.com/abstract=3356325 or http://dx.doi.org/10.2139/ssrn.3356325