精神病基因组学的深度学习:从工具到应用
Junhao Liu1, Siwei Xu1, Dongbo Sun1
1Department of Computer Science, University of California, Irvine, 6210 Donald Bren Hall, Irvine, 92697 CA, USA.
Current opinion in genetics & development
|February 14, 2026
概括
深度学习 (DL) 推进了复杂精神疾病的基因组分析. 这些人工智能工具有助于解释非编码遗传变异,为新的治疗策略铺平了道路.
科学领域:
- 基因组学就是基因组学.
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 精神疾病具有复杂的遗传结构,在非编码DNA中存在许多风险变异.
- 了解大脑中这些非编码变体的调节效应是一个重大挑战.
研究的目的:
- 审查深度学习 (DL) 在精神病基因组学中的演变和应用.
- 要强调DL如何,包括基础模型,解决解释非编码遗传变异的挑战.
主要方法:
- 在基因组学中追踪DL模型的发展,从任务特定到基础模型.
- 检查基因组语言模型,单细胞基础模型和大型语言模型.
- 在精神病基因组学研究中调查DL应用.
主要成果:
- DL提供了强大的工具来分析复杂的遗传数据,并了解监管后果.
- 在生物数据上进行预训练的基础模型提供了解释基因组语言的新方法.
- 最近的DL进展有助于研究与精神疾病相关的非编码变体.
结论:
- 深度学习代表了精神病基因组学研究中的范式转变.
- 这些先进的AI方法可以弥合遗传发现和治疗策略之间的差距.
- 研究人员可以利用DL来更好地理解和治疗精神疾病.
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