Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response - Xu, Xiaolu; Gu, Hong; Wang, Yang; Wang, Jia; Qin, Pan (Frontiers in Genetics, 2019)
Anticancer drug responses can be various for patient individuals. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to construct classification models to predict the drug response. This research focuses on the feature selection issue for classification models. Because the feature space for predicting drug response is of vast dimensions, the autoencoder is first built, and a subset of inputs with the important contribution is selected. Then by using Boruta algorithm, a further small set of features is determined for random forest, which is used to predict drug response. Two datasets, GDSC and CCLE, are used to illustrate the efficiency of the proposed method