Abstract
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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.