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.
Chen, H., Jin, Y., Wang, L., Chi, C. & Shen, J. (2020). HIME: Mining and Ensembling Heterogeneous Information for Protein Interaction Predictions. IEEE International Joint Conference on Neural Networks (pp. 1-8). United States: IEEE.