Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer

Abstract A major challenge in cancer treatment is predicting the clinical response to anticancer drugs for each individual patient. For complex diseases, such as cancer, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the disease process at the molecular level. While the omics era provides unique opportunities to dissect the molecular features of diseases, the ability to apply it to targeted therapeutic efforts is hindered by both the massive size and diverse nature of the omic data. Recent advances with Deep Learning Neural Networks (DLNN), suggests that DLNN could be trained on large data sets to efficiently predict therapeutic responses. We present the application of Association Rule Mining (Market Basket Analysis) in combination with Deep Learning to integrate and extract knowledge in the…


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