A Deep Learning Architecture For Protein-Protein Interaction Article Identification

In recent past there has been phenomenal growth in biomedical literature and health care records. Robust text mining techniques are essential in order to properly organize the documents as well as to extract relevant information. Traditional techniques for document classification focus on machine learning algorithms where learning of classifier is decided on the basis of labeled data and the features that are prominent. In this paper we focus on developing an automated technique for classifying biomedical articles containing protein-protein interaction related information against the others. Our proposed approach is based on deep neural network framework. We investigate the role of convolution neural network (CNN) and propose two model variants. We evaluate the proposed approach on the benchmark datasets of BioCreative-IT Interaction Article Subtask (JAS) data sets. Effectiveness of our proposed model is evident with the significant performance gains, 2.8 % in terms of F-measure and 5 % in terms of accuracy over the traditional models.