Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts
This work addresses the need for efficient information extraction systems in biomedical research, offering a novel method that improves performance on benchmark datasets.
The paper tackles the problem of extracting protein-protein interaction information from biomedical texts by proposing a novel deep bidirectional LSTM model that leverages word sequences and dependency paths, achieving state-of-the-art F1-scores of 86.45% on AiMed and 77.35% on BioInfer datasets.
Knowledge about protein-protein interactions is essential in understanding the biological processes such as metabolic pathways, DNA replication, and transcription etc. However, a majority of the existing Protein-Protein Interaction (PPI) systems are dependent primarily on the scientific literature, which is yet not accessible as a structured database. Thus, efficient information extraction systems are required for identifying PPI information from the large collection of biomedical texts. Most of the existing systems model the PPI extraction task as a classification problem and are tailored to the handcrafted feature set including domain dependent features. In this paper, we present a novel method based on deep bidirectional long short-term memory (B-LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text. This model leverages joint modeling of proteins and relations in a single unified framework, which we name as Shortest Dependency Path B-LSTM (sdpLSTM) model. We perform experiments on two popular benchmark PPI datasets, namely AiMed & BioInfer. The evaluation shows the F1-score values of 86.45% and 77.35% on AiMed and BioInfer, respectively. Comparisons with the existing systems show that our proposed approach attains state-of-the-art performance.