InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information

Meijing Li; Yingying Jiang; Keun Ho Ryu

doi:10.3389/fgene.2022.827540

InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information

Front Genet. 2022 Mar 28:13:827540. doi: 10.3389/fgene.2022.827540. eCollection 2022.

Authors

Meijing Li¹, Yingying Jiang¹, Keun Ho Ryu^{2

3

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Affiliations

¹ College of Information Engineering, Shanghai Maritime University, Shanghai, China.
² Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Vietnam.
³ Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand.
⁴ Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Korea.

Abstract

Protein-protein interaction (PPI) prediction is meaningful work for deciphering cellular behaviors. Although many kinds of data and machine learning algorithms have been used in PPI prediction, the performance still needs to be improved. In this paper, we propose InferSentPPI, a sentence embedding based text mining method with gene ontology (GO) information for PPI prediction. First, we design a novel weighting GO term-based protein sentence representation method to generate protein sentences including multi-semantic information in the preprocessing. Gene ontology annotation (GOA) provides the reliability of relationships between proteins and GO terms for PPI prediction. Thus, GO term-based protein sentence can help to improve the prediction performance. Then we also propose an InferSent_PN algorithm based on the protein sentences and InferSent algorithm to extract relations between proteins. In the experiments, we evaluate the effectiveness of InferSentPPI with several benchmarking datasets. The result shows our proposed method has performed better than the state-of-the-art methods for a large PPI dataset.

Keywords: gene ontology; infersent; protein-protein interaction; sentence representations; text mining.