Machine Learning Methods for Protein Function Prediction

Abstract

AbstractPrediction of protein functions is one of the major challenges of the post-genomic era. Comprehension of protein functions has an important role in understanding the complicated mechanisms in various organisms. Different projects have identified novel protein sequences through high-throughput experiments, and structural genomics approaches are being used to identify their 3D structures. The generation of information about protein function could increase biological understanding and drug development as many diseases change these protein functions. In-silico protein function prediction is a difficult task due to the diversity of protein functions and multi-functionality in proteins. There is a requirement for annotations in a standard and machine-readable form to integrate them into workflows. The quality of the predictions also needs to be checked. Machine learning methods can integrate different features in workflows, give accurate predictions, and also measure the quality of predictions. Machine learning methods are also more useful as compared to traditional methods due to their ability to understand input–output relation without using a fixed model and work efficiently using noisy and non-linear data. In this article, we review the application of different machine learning methods for protein function prediction.KeywordsProtein functionMachine learningIn-silicoPrediction