结构生物信息学的深度学习:当前的应用和未来的前景
Niranjan Kumar1, Rakesh Srivastava2
1School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
Briefings in bioinformatics
|May 3, 2024
概括
深度学习 (DL) 正在通过分析生物学和医疗保健中的大数据来彻底改变结构生物信息学. 本综述详细介绍了从基础到高级的神经网络的DL应用,用于理解生物分子结构.
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:
- 生物数据的指数增长需要先进的计算方法.
- 深度学习 (DL) 为分析复杂的生物信息提供了强大的工具.
- 结构生物信息学是受益于这些进展的关键领域.
研究的目的:
- 审查深度学习对结构生物信息学的影响.
- 提供关于DL在分析生物分子结构中的应用的见解.
- 讨论DL在这个领域的挑战和未来方向.
主要方法:
- 对结构生物信息学深度学习现有文献的审查.
- 解释各种深度学习模型 (例如神经网络,卷积网络,变压器网络).
- 展示DL在理解蛋白质结构和其他生物分子数据中的应用.
主要成果:
- 深度学习正在改变结构生物信息学,由大数据和计算资源驱动.
- 包括先进神经网络在内的DL模型在分析生物分子结构方面取得了重大成功.
- DL正在成为医疗保健和生物数据分析的组成部分.
结论:
- 深度学习是结构生物信息学正在进行的革命中的一个关键技术.
- 持续开发DL模型将进一步提高我们对生物分子结构的理解.
- 整合DL有望彻底改变生物学和医学中的分析过程.
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