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Predicting the regulatory genome
DOI:10.1038/s41576-025-00887-2.png)
摘要
En 中文
深度学习模型在解码调控基因组方面取得了令人瞩目的进展,但关键挑战仍未解决。在本篇评论中,作者概述了从基因组序列预测调控功能的最新深度学习模型,并重点介绍了未来的关键议题,包括专用模型与通用模型之间的权衡、跨细胞类型的多元任务以及针对遗传变异和不同物种的训练。
Keyword:
deep learning
regulatory genome
genomic sequence
multitasking
genetic variation
期刊
IF:
52
论文数:
4.0K
被引数:
4.3W
机构
引用论文
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation基于DNA序列预测RNA-seq覆盖度:一种基因调控的统一模型
Evaluating the representational power of pre-trained DNA language models for regulatory genomics. Genome Biol. 26 (1), 203评估预训练DNA语言模型在调控基因组学中的表示能力。Genome Biol. 26 (1), 203
Predicting effects of noncoding variants with deep learning-based sequence model基于深度学习的序列模型预测非编码变体的效果
NATURE METHODS
IF32.1
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networksBasset: 使用深度卷积神经网络学习可访问基因组的监管代码
GENOME RESEARCH
IF5.5
A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome
NUCLEIC ACIDS RESEARCH
IF13.1
Nucleotide Transformer: building and evaluating robust foundation models for human genomics核苷酸转换器: 构建和评估人类基因组学的稳健基础模型
NATURE METHODS
IF32.1
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