Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine

Haijun Lei, Yuting Wen, Zhuhong You, Ahmed Elazab, Ee Leng Tan, Yujia Zhao, Baiying Lei

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32 引用 (Scopus)

摘要

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e.g., hydrophobicity and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.

源语言英语
文章编号8382154
页(从-至)1290-1303
页数14
期刊IEEE Journal of Biomedical and Health Informatics
23
3
DOI
出版状态已出版 - 5月 2019
已对外发布

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