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Unlocking gene regulation with sequence-to-function models
DOI:10.1038/s41592-024-02331-5.png)
Abstract
En 中文
By exploiting recent advances in modern artificial intelligence and large-scale functional genomic datasets, sequence-to-function models learn the relationship between genomic DNA and its multilayer gene regulatory functions. These models are poised to uncover mechanistic relationships across layers of cellular biology, which will transform our understanding of cis gene regulation and open new avenues for discovering disease mechanisms.
Keywords:
GENOME
Journal
IF:
32.1
Papers:
7.3K
Citations:
12.7W
Organization
Cited Papers
Personal transcriptome variation is poorly explained by current genomic deep learning models
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Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale
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Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings
NATURE GENETICS
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Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk
NATURE GENETICS
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