可以解释的深度学习对OMIC数据分析的承诺:向AI添加新的发现工具
Mariangela Santorsola1, Francesco Lescai1
1Department of Biology and Biotechnology, University of Pavia, Pavia, Italy.
New biotechnology
|June 17, 2023
概括
可解释的深度学习 (EDL) 提高了复杂的生物数据的解释,提供了透明度和新的见解. 这种方法正在为生命科学家的基因组学,药物设计和临床试验等领域带来革命.
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
- 在生命科学领域的人工智能
背景情况:
- 深度学习 (DL) 在处理大型异质数据集方面表现出色,显著影响了omics数据分析.
- 了解复杂的生物系统是一个关键的挑战,由日益增长的OMIC数据量放大.
- 目前的DL方法往往缺乏透明度,对临床环境等关键应用构成挑战.
结论:
- 可解释的深度学习 (EDL) 为解释生物数据提供了强大的范式转变.
- 鼓励生命科学家采用EDL工具,以更深入地理解和发现.
- 建议提供资源,以促进EDL在研究中的首次采用.
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