Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.

ABSTRACT: Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). We constructed a model that successfully predicts clinical drug response [area under the curve (AUC) = 0.76] and show that even with limited sample size, integrating high-dimensional genetics data with clinical data

SUBMITTER: de Jong J 

PROVIDER: S-EPMC8320273 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature