부산대학교 도서관

Explainable Artificial Intelligence (XAI) is a field that develops ways to explain predictions made by AI models. In this paper XAI which is a multifaceted approach is discussed which is capable of defining the value of features while producing predictions. Precision medicine and the forecast of cancer’s reaction to a specific treatment or drug efficiency is an area of active research. Drug sensitivity forecasting on massive genomics data is a strenuous process in drug discovery. However, drug personalization on the other hand is a tedious and arduous matter. Explainable AI is one of the many properties that instills confidence and dependency in AI systems which is why more attention needs to be paid to XAI. This research is a step toward a more profound understanding of deep learning techniques [1] on gene expressions and drug chemical structures.