在建模和分析大遗传数据的本体学
N L Podkolodnyy1, O A Podkolodnaya2, V A Ivanisenko3
1Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia.
Vavilovskii zhurnal genetiki i selektsii
|February 13, 2025
概括
新的基于本体学的深度学习方法通过实现实验数据的语义整合和大数据的可解释分析来增强生物信息学和生物医学. 这些方法提高了对基因组学,系统生物学和诊断的理解.
科学领域:
- 生物信息学和生物医学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 医疗保健中的人工智能
背景情况:
- 生物信息学和生物医学中的大量实验数据需要先进的系统化和利用.
- 现有的方法在异质数据的语义整合和深度学习模型的可解释性方面扎.
- 实体学和知识图为组织和分析复杂的生物信息提供了一个框架.
研究的目的:
- 提出新的基于本体学的方法,用于对异质实验数据的语义整合.
- 为大数据分析引入创建大型知识库和自我解释深度学习模型的方法.
- 证明本体论在提高生物应用中的深度学习的可解释性和性能方面的实用性.
主要方法:
- 使用了对本体论的正式定义和知识图表来进行数据集成.
- 开发了用于语义集成和知识库创建的自动化方法.
- 实施基于本体学的深度学习模型,包括Deep GONet和ONN4MST,用于生物数据分析和解释.
主要成果:
- 基因本体学 (GO) 和其因果影响模型 (GO-CAM) 作为基因组学和系统生物学的一个成功例子.
- 生物属性本体学 (OBA) 展示了一种使用设计模式和自动推理构建大型本体学的方法.
- 深度GONet在癌症诊断中实现了高性能和可解释性,而ONN4MST使用生物体本体学有效追踪微生物来源.
结论:
- 本体学驱动的深度学习模型为分析大规模生物数据提供了强大的,可解释的工具.
- 这些方法解决了深度学习中的"黑子"问题,使生物洞察力更好地理解.
- 提出的方法对于推进基因组学,系统生物学,诊断和微生物组分析方面的研究是有价值的.
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