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  "Description": "Implements a tree-based method specifically designed for\npersonalized medicine applications. By using genomic and\nmutational data, 'ODT' efficiently identifies optimal drug\nrecommendations tailored to individual patient profiles. The\n'ODT' algorithm constructs decision trees that bifurcate at\neach node, selecting the most relevant markers (discrete or\ncontinuous) and corresponding treatments, thus ensuring that\nrecommendations are both personalized and statistically robust.\nThis iterative approach enhances therapeutic decision-making by\nrefining treatment suggestions until a predefined group size is\nachieved. Moreover, the simplicity and interpretability of the\nresulting trees make the method accessible to healthcare\nprofessionals. Includes functions for training the decision\ntree, making predictions on new samples or patients, and\nvisualizing the resulting tree. For detailed insights into the\nmethodology, please refer to Gimeno et al. (2023)\n<doi:10.1093/bib/bbad200>.",
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  "Date/Publication": "2025-03-25 09:47:41 UTC",
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