Prediction of interactions between viral and host proteins using supervised machine learning methods.

ABSTRACT:

Background

Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.

Methods

In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.

Results

Out o

SUBMITTER: Barman RK 

PROVIDER: S-EPMC4223108 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature