人工智能和深度学习算法用于表观遗传序列分析:对表观遗传学家和人工智能专家的一篇评论
Muhammad Tahir1, Mahboobeh Norouzi1, Shehroz S Khan2
1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T 5V6, MB, Canada.
Computers in biology and medicine
|November 5, 2024
概括
人工智能 (AI) 和机器学习加速了表观遗传学的研究,这涉及在不改变DNA序列的情况下改变基因表达的变化. 这篇评论为研究人员和临床医生对表观遗传挑战的AI解决方案绘制了地图.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 表观遗传学通过DNA甲基化,基因素修饰和非编码RNA来调节基因表达.
- 表观遗传改变与癌症和先天性形等疾病有关.
- 传统的实验性表观遗传学研究是耗时且昂贵的.
研究的目的:
- 对表观遗传学数据应用的AI模型进行审查,以了解基因调节和疾病.
- 为人工智能研究人员提供表观遗传学问题的分类.
- 为表观遗传学家提出AI解决方案,并确定研究缺口.
主要方法:
- 在表观遗传学中对人工智能发表的研究的叙述性审查.
- 在表观遗传学数据上训练的AI模型的分析.
- 人工智能应用在表观遗传学研究中的分类.
主要成果:
- 人工智能有效地预测疾病标记物,基因表达,增强剂-促进剂相互作用和染色质状态.
- 确定了各种适用于特定表观遗传学问题的AI方法.
- 强调了人工智能驱动表观遗传学的现有差距,挑战和未来研究方向.
结论:
- 人工智能和机器学习提供了强大的工具来克服实验表观遗传学研究的局限性.
- 将人工智能专业知识与表观遗传学知识相结合,对于推动该领域的发展至关重要.
- 未来的研究应该专注于解决已识别的挑战,以充分利用AI在表观遗传学.
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