可解释性
计算生物学
生物
表观遗传学
调节顺序
人类基因组
基因表达调控
基因组
基因
转录调控
序列(生物学)
鉴定(生物学)
基因组学
DNA测序
遗传学
基因表达
计算机科学
人工智能
植物
作者
Ksenia Sokolova,Kathleen Chen,Yun Hao,Jian Zhou,Olga G. Troyanskaya
出处
期刊:Annual Review of Genomics and Human Genetics
[Annual Reviews]
日期:2024-04-09
标识
DOI:10.1146/annurev-genom-021623-024727
摘要
Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.
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