Drug and disease similarity calculation platform for drug repositioning Drug repositioning, aiming to infer potential indications for drugs efficiently, has achieved remarkable results in reducing the cycle, cost and risk of drug Research and Development (Ru0026D), and mining new uses of known drugs. Currently, many computational drug repositioning strategies have been proposed. The similarity calculation, as one of the key steps of drug repositioning, has an important impact on the accuracy of computational drug repositioning. However, the biological data used for the similarity calculation come from a wide range of sources with different formats, and similarity calculation methods are developed in different programming languages, thus the similarity calculation methods are varying. To facilitate the similarity calculation for drug repositioning, we developed a computational platform consisting of various datasets and similarity measures for drugs and diseases, and four programming languages (Java, R, Python and MATLAB) are supported by our platform. Users can use relevant data and methods directly according to their needs and customize similarity calculation methods. The platform is available at: http:bioinformatics.csu.edu.cnartemis.

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.