August 2019 in “bioRxiv (Cold Spring Harbor Laboratory)” This study developed the CATNIP computational model, which uses biological and chemical information to successfully identify drug repurposing opportunities for various conditions, including Parkinson’s disease and Type 2 Diabetes.
8 citations
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August 2020 in “PLOS Computational Biology” This study presents a computational approach, CATNIP, which repurposes drugs using only their biological and chemical information, predicting new uses like adrenergic uptake inhibitors for Parkinson's and vandetanib for Type 2 Diabetes.
July 2023 in “Journal of Biomedical Science” In this review, the authors emphasize that phenotypic heterogeneity in genetic systems and human diseases is influenced by stochastic fluctuation and network topology, proposing that ultrasensitivity and threshold effects explain this variability, which may inform strategies for preventing and treating genetic diseases.
158 citations
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January 2015 in “Artificial Intelligence in Medicine” This study found that DrugNet, a network-based prioritization method, effectively improves drug repositioning tasks, achieving high performance in validation tests and clinical trial comparisons.
2 citations
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November 2025 in “Briefings in Bioinformatics” In this study, the researchers performed a comparative analysis of drug-target interaction data from multiple databases to refine drug repurposing strategies, revealing potential associations between drug characteristics and therapeutic groups, and predicting repositioning opportunities for FDA-approved drugs across major cancer types.