A Systematic Framework for Drug Repurposing based on Literature Mining Drug repurposing, referring to the discovery of new indications for an existing drug, has become an important aspect of drug development. This study seeks to extract hidden diseasedrug associations from existing literature. Specifically, we propose a systematic framework assuming that a drug has a new use for a certain disease if the clinical disease symptoms and clinical drug effects complement each other. Via literature mining, we generate two vectors to describe clinical disease symptoms and clinical drug effects. Then we use a logistic regression model to predict how effectively the drug counteracts the disease based on the generated diseasedrug vectors. Comprehensive experiments show the effectiveness of proposed framework. According to our results, there are many hidden diseasedrug associations which can be the candidates for drug repurposing.

Predicting DrugDisease Treatment Associations Based on Topological Similarity and Singular Value Decomposition To assist drug development, many computational methods have been proposed to identify potential drugdisease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods need the similarities of drugs and diseases, and they will not work if the biological or chemical features for computing similarities are missing. Besides, being lack of validated negative samples in the drugdisease associations data, most methods simply select some unlabeled samples as negative ones, which may introduce noises. Herein, we propose a new method (TSSVD) which only uses those known drugprotein, diseaseprotein and drugdisease interactions to predict the potential drugdisease associations. In a constructed drugproteindisease heterogeneous network, we consider the common neighbors of drugs and diseases to obtain the topological similarity. Then the topological similarity matrix of drugs (diseases) will be used to get the low dimensional embedding representations of drugdisease pairs. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the kstep neighbors relationships between drugs and diseases. Compared with some stateoftheart methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods.