Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction
Oleksandr Narykov, Yitan Zhu, Thomas Scott Brettin, Yvonne A. Evrard, Alexander Partin, Maulik P. Shukla, Priyanka Vasanthakumari, James H. Doroshow, Rick L. Stevens · 2023
This work studies a particular setting for regression problems – tasks with complex combinatorial data space where samples can be divided into distinct groups. Anti-cancer drug response prediction is a perfect example of this setting, in which each sample includes cancer biological features and drug chemical information. Many existing works of pan-drug and pan-cancer response modeling treat different combinations of drugs and cancers as individual samples. A potential problem in these works is that a model may be heavily influenced and biased toward overrepresented drugs and cancers. Our work develops a method to solve this issue by adjusting the model training process in a deep learning framework.