在生物信息学中建立基础模型
Fei Guo1,2, Renchu Guan3, Yaohang Li4
1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
National science review
|March 13, 2025
概括
基础模型 (FMs) 正在通过高效地分析基因组学,蛋白质组学和药物发现的大数据集来彻底改变生物信息学. 这篇评论指导科学家在为计算生物学进步选择FM时.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 基础模型 (FMs) 在生物信息学中变得越来越重要,解决预培训,评估和可解释性方面的挑战.
- FMs擅长处理大型,未标记的生物数据集,克服昂贵的实验程序的局限性.
- 这些模型在代表生物实体方面表现出高度准确性,推动了计算生物学方面的创新.
研究的目的:
- 审查应用到生物信息学基础模型 (FMs) 的最新进展.
- 为科学家在选择适合各种生物信息学任务的FM指导.
- 突出人工智能在促进分子生物学和理解分子景观方面的作用.
主要方法:
- 关于生物信息学基础模型的最新文献的综述.
- 将FM分类为语言,视觉,图形和多式联络类型.
- 在基因组学,转录组学,蛋白质组学,药物发现和单细胞分析中分析FM应用.
主要成果:
- 在各种下游生物信息学任务中,FM已经取得了显著的成功.
- 在生物实体表示和大数据集管理方面表现出高准确度.
- 确定了适用于生物信息学挑战的四种主要类型的FM.
结论:
- 基础模型代表了计算生物学新时代,为科学发现提供了强大的工具.
- 通过FM,人工智能为分子生物学中的持续创新提供了基础.
- 有效的FM选择对于最大限度地提高它们在生物信息学研究中的影响至关重要.
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