Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein-protein interaction

Knowledge about protein-protein interactions is essential for 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 not yet accessible as a structured database. Thus, efficient information extraction systems are required for identifying PPI information from the large collection of biomedical texts. In this paper, we present a novel method based on attentive deep recurrent neural network, which combines multiple levels of representations exploiting word sequences and dependency path related information to identify protein-protein interaction (PPI) information from the text. We use the stacked attentive bi-directional long short term memory (Bi-LSTM) as our recurrent neural network to solve the PPI identification problem. This model leverages joint modeling of proteins and relations in a single unified framework, which is named as the 'Attentive Shortest Dependency Path LSTM' (Att-sdpLSTM) model. Experimentation of the proposed technique was conducted on five popular benchmark PPI datasets, namely AiMed, Biolnfer, HPRD50, IEPA, and LLL The evaluation shows the F1-score values of 93.29%, 81.68%, 78.73%, 76.25%, & 83.92% on AiMed, Biolnfer, HPRD50, IEPA, and LLL dataset, respectively. Comparisons with the existing systems show that our proposed approach attains state-of-the-art performance. (C) 2018 Elsevier B.V. All rights reserved.