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...
Main Authors: | Sejin Park, Jihee Soh, Hyunju Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2021-05-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-021-04146-z |
Similar Items
-
Pharmaco-Omics in Psoriasis: Paving the Way towards Personalized Medicine
by: Charalabos Antonatos, et al.
Published: (2023-04-01) -
Obtaining triplet-triplet absorption spectra and triplet lifetimes of long-lived molecules with a UV-Visible spectrophotometer
by: Tiago Palmeira, et al.
Published: (2024-04-01) -
Applications and Prospects for Triplet–Triplet Annihilation Photon Upconversion
by: Martin P. Rauch, et al.
Published: (2018-08-01) -
Synthesis and Photophysics of Phenylene Based Triplet Donor–Acceptor Dyads: ortho vs. para Positional Effect on Intramolecular Triplet Energy Transfer
by: Young Ju Yun, et al.
Published: (2022-06-01) -
A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems
by: Wenke Zang, et al.
Published: (2017-06-01)