A Protein-Protein Interaction Extraction Approach Based on Large Pre-trained Language Model and Adversarial Training

• Zhan Tang,
• Xuchao Guo,
• Zhao Bai,
• Lei Diao,
• Shuhan Lu,
• Lin Li,

Vol. 16, No. 3, March 31, 2022
10.3837/tiis.2022.03.002, Download Paper (Free):

• adversarial training
• Information Extraction
• Natural Language Processing
• pre-trained language model
• protein-protein interaction

Abstract

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

Statistics

Cite this article