基础模型在生物信息学中的进展和机遇
Qing Li1, Zhihang Hu1, Yixuan Wang1
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, 999077, China.
Briefings in bioinformatics
|October 26, 2024
概括
基础模型 (FMs) 正在通过克服数据限制和噪声来革命生物信息学. 本调查探讨了它们在计算生物学中的应用,挑战和未来方向.
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:
- 生物信息学面临的挑战是有限的注释数据和数据噪声.
- 基础模型 (FMs) 代表了人工智能 (AI) 在生物研究中的重大进步.
- FM为各种复杂的生物数据提供了新的解决方案.
研究的目的:
- 调查和总结FMS在生物信息学中的应用.
- 追踪生物学的FMs的演变,当前的景观和方法.
- 引导研究人员选择适合特定生物任务的FM.
主要方法:
- 在生物信息学中研究FM的进化轨迹.
- 分析FMS对生物问题的应用,如序列分析,结构预测和函数注释.
- 与传统方法对比FM架构和进步.
主要成果:
- FMs在下游验证任务中表现出有效性,代表着各种生物实体.
- 在各种生物领域,FM比传统方法提供了更好的性能.
- 确定的挑战包括数据噪声,模型解释性和FM应用中的潜在偏差.
结论:
- FMs正在开启计算生物学新时代.
- 了解FM的局限性对于最佳性能至关重要.
- 本次审查为未来的FM开发和生物研究中的应用提供了路线图.
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