人工智能 (AI) 模型和生成算法的进步代表了基因组学研究的新范式
Du Hyeong Lee1,2, Eun Gyung Park1,2, Yun Ju Lee1,2
1Department of Integrated Biological Science, Pusan National University, Busan 46241, Republic of Korea.
International journal of molecular sciences
|November 27, 2025
概括
人工智能 (AI) 正在通过对庞大的数据集进行复杂分析来彻底改变基因组学. 先进的AI模型现在可以预测基因组功能,甚至可以设计新的DNA序列,从而增强基因组解释.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因组分析与计算能力一起进步,从基本统计数据转向复杂的人工智能 (AI).
- 减少测序成本已经产生了大量,多样化的基因组数据集,推动了该领域的AI开发.
- 人工智能越来越多地应用于预测基因组特征,如转录因子结合和DNA甲基化.
研究的目的:
- 审查计算进步对基因组分析的影响.
- 要突出AI在基因组学中的扩展应用.
- 讨论生成AI在模拟和设计基因组序列方面的潜力.
主要方法:
- 利用先进的AI架构,如深度神经网络,卷积神经网络,循环神经网络和变压器.
- 整合多学科数据以提高预测准确度.
- 开发用于基因组序列模拟和设计的生成性AI模型.
主要成果:
- 人工智能模型现在可以有效地预测转录因子结合部位,表观遗传元素,DNA甲基化和非编码序列函数.
- 现代人工智能架构可以处理更长的基因组输入,捕获远程基因组上下文,并集成多种多omics数据.
- 生成性人工智能已经证明了模拟和设计功能性基因组序列的能力.
结论:
- 人工智能,特别是深度学习和生成模型,正在改变基因组分析和解释.
- 这些计算进步加速了对基因组数据的理解和操纵.
- 人工智能的整合有望显著提高我们导航和利用基因组景观的能力.
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