A multi-view deep neural network model for chemical-disease relation extraction from imbalanced datasets

Understanding the chemical-disease relations (CDR) is a crucial task in various biomedical domains. Manual mining of these information from biomedical literature is costly and time-consuming. To address these issues, various researches have been carried out to design an efficient automatic tool. In this paper, we propose a multi-view based deep neural network model for CDR task. Typically, multiple representations (or views) of the datasets are not available for this task. So, we train multiple conceptually different deep neural network models on the dataset to generate different abstract features, treated as different views. A novel loss function, 'Penalized LF', is defined to address the problem of imbalance dataset. The proposed loss function is generic in nature. The model is designed as a combination of Convolution Neural Network (CNN) and Bidirectional Long Short Term Memory (Bi-LSTM) network along with a Multi-Layer Perceptron (MLP). To show the efficacy of our proposed model, we have compared it with six baseline models and other state-of-the-art techniques, on 'chemicals-and-disease-DFE' dataset, a free text dataset created by Li et al. from BioCreative V Chemical Disease Relation dataset. Results show that the proposed model attains highest F1-score for individual classes, proving its efficiency in handling class imbalance problem in the dataset. To further demonstrate the efficacy of the proposed model, we have presented results on BioCreative V dataset and two Protein-Protein Interaction Identification (PPI) datasets, viz., AiMed and BioInfer. All these results are also compared with the state-of-the-art models.