Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.

ABSTRACT:

Background

Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response.

Results

We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary clas

SUBMITTER: Park S 

PROVIDER: S-EPMC8152321 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature