癌症研究中的深度学习模型:一项调查
IEEE transactions on computational biology and bioinformatics
|November 20, 2025
概括
本综述对癌症研究中的途径信息化深度学习模型进行了分类,详细说明了途径信息的应用. 它强调了开发更好的模型的策略,优点,缺点和挑战.
科学领域:
- 计算生物学和生物信息学
- 深度学习在瘤学中的应用.
背景情况:
- 生物学途径对于理解复杂疾病,药物发现和个性化医学至关重要.
- 结合途径信息的深度学习模型由于可解释性和性能,在癌症研究中表现有前途.
研究的目的:
- 系统地审查和分类用于癌症研究的路径信息深度学习模型.
- 在这些模型中分析应用路径信息的策略.
- 确定通用路径数据库和可解释性方法.
主要方法:
- 癌症研究中的通路信息深度学习模型的综合调查.
- 将路径信息分为四类的分类.
- 基于路径数据利用和应用策略,将模型分为三个主要类型和七个子类别.
主要成果:
- 路径信息应用策略为每个子类别所示.
- 总结了不同路径信息模型的优缺点.
- 识别了常用的路径数据库和可解释性技术.
结论:
- 该综述提供了癌症研究中途径信息深度学习的结构化概述.
- 它提供了对模型设计,战略选择的见解,并确定了当前的挑战.
- 这项工作旨在指导开发更有效的路径信息深度学习模型.
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