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

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 te...

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Bibliographic Details
Main Authors:	Lee, H. (Author), Park, S. (Author), Soh, J. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Binary classification Biological Phenomena Cancer therapeutics Cell culture cellular, subcellular and molecular biological phenomena and functions Copy number aberrations Cross-validation tests Deep learning Diseases drug Drug response prediction encoder using supervised methods Extraction Feature extraction Forecasting Gene expression Gene Expression Data human Humans Learning systems Multi-omics data mutation Mutation neoplasm Neoplasms personalized medicine Pharmaceutical Preparations Pharmacogenomics Precision Medicine Precision oncology Prediction accuracy Signal encoding Supervised feature extractions Supervised methods Triplet loss
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Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

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