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HIME: Mining and Ensembling Heterogeneous Information for Protein Interaction Predictions

Conference Paper


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Abstract


  • 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.

UOW Authors


  •   Chen, Huaming (external author)
  •   Jin, Yaochu (external author)
  •   Wang, Lei
  •   Chi, Chi-Hung (external author)
  •   Shen, Jun

Publication Date


  • 2020

Citation


  • 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.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5228&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4201

Start Page


  • 1

End Page


  • 8

Place Of Publication


  • United States

Abstract


  • 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.

UOW Authors


  •   Chen, Huaming (external author)
  •   Jin, Yaochu (external author)
  •   Wang, Lei
  •   Chi, Chi-Hung (external author)
  •   Shen, Jun

Publication Date


  • 2020

Citation


  • 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.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5228&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4201

Start Page


  • 1

End Page


  • 8

Place Of Publication


  • United States