Constructing a PPI Network Based on Deep Transfer Learning for Protein Complex Detection

Xin Yuan, Hangyu Deng, Jinglu Hu*

*この研究の対応する著者

研究成果: Article査読

抄録

In the detection of protein complexes, the completeness of a protein–protein interaction (PPI) network is crucial. Complete PPI networks, however, are not available for most species because experimentally identified PPIs are usually very limited. This paper proposes a deep learning based PPI predictor to construct a complete PPI network, from which protein complexes are detected using a spectral clustering method. For this purpose, the unknown PPIs are estimated by using a deep PPI predictor consisting of a semi-supervised SVM classifier and a deep feature extractor of the convolutional neural network (CNN). Meanwhile, the similarities of gene ontology (GO) annotations contribute to protein interactions, and the differences of subcellular localizations contribute to negative interactions. Considering that, we pretrain the deep CNN feature extractor in a class of deep GO annotation and subcellular localization predictors using datasets from the type species, then transfer it to the PPI prediction model for fine-tuning. In this way, we have a deep PPI detector enhanced with transfer learning of GO annotation and subcellular localization prediction. Experimental results on benchmark datasets show that the proposed method outperforms the state-of-the-art methods.

本文言語English
ページ(範囲)436-444
ページ数9
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
17
3
DOI
出版ステータスPublished - 2022 3月

ASJC Scopus subject areas

  • 電子工学および電気工学

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