Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches
Abstract
Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions. In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods. We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions. The findings of this review confirm the importance of computational met...Continue Reading
References
Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines
A viral-human interactome based on structural motif-domain interactions captures the human infectome
Prediction of interactions between viral and host proteins using supervised machine learning methods
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