Publication
Title
The pitfalls of negative data bias for the T-cell epitope specificity challenge
Author
Abstract
Recently, Gao et al. introduced a combination of meta-learning and the neural Turing machine to tackle a very important but yet unsolved problem in immunology: the TCR–epitope binding prediction challenge for novel epitopes. All high-performing machine learning models can have problems when deployed in a real-world setting if the data used to train and test the model contains biases. In this article, we describe how the technique used to create negative data for the TCR–epitope interaction prediction task can lead to a strong bias and makes that the performance drops to random when tested in a more realistic scenario.
Language
English
Source (journal)
Nature Machine Intelligence
Publication
2023
ISSN
2522-5839
DOI
10.1038/S42256-023-00727-0
Volume/pages
5 :10 (2023) , p. 1060-1062
ISI
001129678300003
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 06.10.2023
Last edited 06.04.2024
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