A Framework for Personalized Medicine with Reverse Phase Protein Array and Drug Sensitivity

In this paper, we propose a framework for personalized medicine with Reverse-Phase Protein Array (RPPA) and drug sensitivity. The goal of personalized medicine is to provide an optimal drug to a patient by predicting the drug sensitivity. For the prediction, our method is based on naive Bayes classifier assuming that all features (proteins) are independent. Once the classifier is trained by RPPA data for the cancer, the sensitivity of target drug is predicted for a patient's sample. As a result, a set of drugs that has low sensitivity can be provided to a patient. Through this individualized therapy, the patients can be treated more effectively without the risk of wasting time and cost. In addition, we explore if naive Bayes classifier can be improved by using the dependency between features. To this end, learning Bayesian network is performed to infer the dependency map, and then the selected edges from the estimated network model is combined with the network structure of naive Bayes classifier. As our contribution, the experiments with lung cancer data prove that RPPA data can be used to profile patient for drug sensitivity prediction, and also our proposed personalize medicine system achieved approximately 94\% prediction accuracy.