我们是怎么到达那里的? 对生物网络和序列的AI应用
Marco Anteghini1, Francesco Gualdi2, Baldo Oliva3
1BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy; Visual and Data-Centric Computing, Zuse Institut Berlin, Berlin, Germany.
Computers in biology and medicine
|April 4, 2025
概括
人工智能 (AI) 正在通过分析基因组学,蛋白质组学和系统生物学中的复杂数据来彻底改变生物学. 人工智能增强了序列嵌入,动机发现和网络分析,为生物过程和疾病提供了新的见解.
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.
- 生命科学中的人工智能
背景情况:
- 生物数据的指数增长需要先进的分析工具.
- 人工智能 (AI) 为解释复杂的生物信息提供了强大的方法.
- 基因组学,蛋白质组学和系统生物学产生了庞大的数据集,需要复杂的分析.
研究的目的:
- 提供现代生物研究中人工智能方法的全面概述.
- 突出AI在分析生物数据,包括序列和网络中的作用.
- 讨论AI在生物学中的当前应用和未来方向.
主要方法:
- 审查机器学习算法,重点关注深度学习模型.
- 在序列嵌入,动机发现和结构预测方面分析AI应用.
- 在生物网络分析 (蛋白质与蛋白质相互作用,多层网络) 中探索人工智能集成.
主要成果:
- 人工智能,特别是深度学习,显著提高了生物数据分析的准确性和效率.
- 人工智能技术可以更好地预测基因表达和蛋白质结构.
- 通过网络分析,人工智能可以更深入地了解复杂的生物过程和疾病机制.
结论:
- 人工智能是从大型生物数据中提取知识的变革性工具.
- 目前的人工智能应用在基因组学,蛋白质组学和系统生物学方面显示出巨大潜力.
- 对人工智能方法的进一步研究将继续推进生物发现和理解.
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