深度学习时代的个性化基因表达预测:一篇评论
Viksar Dubey1, Li Shen2,3
1College of Computing, Data Science, and Society, University of California, 2195 Hearst Avenue, Berkeley, CA 94720, United States.
Briefings in bioinformatics
|January 30, 2026
概括
深度学习模型显示出预测基因表达的前景,但与个体遗传差异作斗争. 当前的线性模型往往优于深度学习,用于个性化的基因表达预测.
科学领域:
- 计算型基因组学计算型基因组学
- 生物信息学是一种生物信息学.
- 基因组数据分析 基因组数据分析
背景情况:
- 从基因组序列预测基因表达是计算基因组学的一个关键挑战.
- 在表观基因组数据上训练的深度学习模型显示出潜力,但受到参考基因组训练的限制.
- 这些模型往往无法捕捉个体遗传变异,影响个性化基因表达预测.
研究的目的:
- 审查最近在预测个性化基因表达方面的进展.
- 将深度学习模型与传统的线性方法进行比较.
- 突出新兴的基因组语言模型和微调策略.
主要方法:
- 关于深度学习和基因表达预测的线性模型的最新文献的综述.
- 参考模型性能与个人基因组的比较.
- 对微调策略和基因组语言模型的分析.
主要成果:
- 深度学习模型在超越线性模型的跨个体基因表达预测方面面临挑战.
- 准确预测个性化的基因表达仍然是一个开放的问题.
- 模型架构和培训方法不断进步.
结论:
- 个性化基因表达预测需要有效捕捉个体遗传变异的模型.
- 虽然深度学习有希望,但线性模型在跨个体预测方面仍然具有竞争力.
- 需要进一步的研究来开发针对个性化的基因表达预测的强大方法.
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