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: Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem.

LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning