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After that, we get low dimensional embedding representations of drugdisease pairs by using topological features and singular value decomposition. 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 k-step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative sample
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