HIME: Mining and Ensembling Heterogeneous Information for Protein Interaction Predictions

esearch on protein-protein interactions (PPIs) data

paves the way towards understanding the mechanisms of infectious

diseases, however improving the prediction performance

of PPIs of inter-species remains a challenge. Since one single

type of sequence data such as amino acid composition may be

deficient for high-quality prediction of protein interactions, we

have investigated a broader range of heterogeneous information

of sequences data. This paper proposes a novel framework for

PPIs prediction based on Heterogeneous Information Mining

and Ensembling (HIME) process to effectively learn from the

interaction data. In particular, the proposed approach introduces

an ensemble process together with substantial features

that generate better performance of PPIs prediction task. The

performance of the proposed framework is validated on real

protein interaction datasets. The extensive experiments show that

HIME achieves higher performance over all existing methods

reported in literature so far.