Special Issue Article Open Access

Protein Structure and Function Prediction Using Machine Learning Methods ? A Review

Abstract

Machine learning is a subfield ofcomputer science that includes the study of systems that can learn from data, rather than follow only explicitly programmed instructions. Some of the most common techniques used for machine learning are Support Vector Machine, Artificial Neural Networks, K Nearest Neighbor and Decision Tree. Machine learning techniques are widely used techniques in bioinformatics to solve different type of problems. Protein structure prediction is one of the problems that can be solved using machine learning. The molecules which are important in our cells are Proteins. They are virtually involved in all cell functions. Proteins are categorized on the basis of the occurrence of conserved amino acid patterns which is the feature extraction method. In the post-genomic era Protein function prediction is an important problem. Advancements in the experimental biology have enabled the production of enormous amount of protein-protein interaction data. Thus, to functionally annotate proteins has been extensively studied using protein-protein interaction data. When annotation and interaction information is inadequate in the networks most of the existing network based approaches do not work well. In this paper an attempt has been made to review different papers on proteins functions and structures that are predicted using the various machine learning methods.

Hemalatha N., Siddhant Naik, Jeason Rinton Saldanha

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