生物学中的人工智能和机器学习:从基因到蛋白质
Zaw Myo Hein1,2, Dhanyashri Guruparan3, Blaire Okunsai3
1Department of Basic Medical Sciences, College of Medicine, Ajman University, Ajman P.O. BOX 346, United Arab Emirates.
Biology
|October 29, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正通过分析复杂的基因组和蛋白质组数据来彻底改变生物学. 这些先进的AI工具加速了基因功能预测,变体识别和蛋白质结构确定,推动了个性化医疗.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 由于复杂性,基因组和蛋白质组数据分析存在重大挑战.
- 传统的方法难以应对生物数据集的规模和复杂性.
- 人工智能 (AI) 的进步为生物数据解释提供了新的范式.
研究的目的:
- 为生物学中人工智能方法提供全面的审查.
- 要突出AI对基因功能,变异识别和蛋白质结构预测的影响.
- 讨论多学科数据的整合和生物研究人工智能的未来挑战.
主要方法:
- 对基础神经网络,变压器架构和大型语言模型 (LLM) 的审查.
- 在基因功能预测,遗传变异识别和蛋白质结构确定 (例如AlphaFold,DeepBind) 中对AI应用的分析.
- 探索用于新型蛋白质和基因组序列设计的生成模型,以及用于多omics数据融合的图形神经网络.
主要成果:
- 人工智能已经彻底改变了基因功能,遗传变异和蛋白质结构/相互作用的预测.
- 生成型人工智能模型在设计新的生物序列方面展示了前所未有的规模和准确性.
- 由人工智能驱动的多omics数据集成为细胞异质性和疾病机制提供了更深入的见解,推动了个性化医学和药物发现.
结论:
- 人工智能,特别是深度学习,是生物科学中的变革力量.
- 人工智能促进了从基因到蛋白质理解生物系统的突破.
- 解决数据质量,可解释性,伦理和计算方面的挑战对于未来的生物学AI进步至关重要.
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