Current approaches to genomic deep learning struggle to fully capture human genetic variation

Tang, Ziqi, Toneyan, Shushan, Koo, Peter K (December 2023) Current approaches to genomic deep learning struggle to fully capture human genetic variation. Nature Genetics, 55 (12). pp. 2021-2022. ISSN 1061-4036

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URL: https://www.ncbi.nlm.nih.gov/pubmed/38036789

DOI: 10.1038/s41588-023-01517-5

Abstract

Deep learning shows promise for predicting gene expression levels from DNA sequences. However, recent studies show that current state-of-the-art models struggle to accurately characterize expression variation from personal genomes, limiting their usefulness in personalized medicine.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics organism description > animal organism description > animal > mammal > primates > hominids organism description > animal > mammal > primates > hominids > human organism description > animal > mammal organism description > animal > mammal > primates
CSHL Authors:	Tang, Ziqi Toneyan, Shushan Koo, Peter K
Communities:	CSHL labs > Koo Lab School of Biological Sciences > Publications
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	December 2023
Date Deposited:	20 Dec 2023 18:46
Last Modified:	29 Feb 2024 18:12
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41332

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