使用多omics整合和临床变量进行癌症生存预测的全面审查
Dao Tran1, Ha Nguyen1, Van-Dung Pham1
1Department of Computer Science and Software Engineering, Auburn University, 345 W Magnolia Avenue, Auburn, AL 36849, United States.
Briefings in bioinformatics
|April 13, 2025
概括
预测癌症患者的结果对于有效治疗至关重要. 本综述探讨了20种方法,整合了多组学和临床数据,以改善生存预测,帮助生命科学家选择资源.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 生物统计学 生物统计学
背景情况:
- 癌症的预后是复杂的,从良性到攻击性形式.
- 使用临床数据的传统预后模型在捕获分子和宿主因素方面存在局限性.
- 准确的结果预测对于定制癌症治疗策略至关重要.
研究的目的:
- 审查20种生存预测方法,这些方法整合了多omics和临床数据.
- 通知生命科学家关于可用的资源和生存预测的关键组成部分.
- 讨论当前方法的优点,缺点和挑战.
主要方法:
- 对20种不同的生存预测方法的审查.
- 分析生存时间建模策略 (连续和离散).
- 检查数据整合技术,预测方法,模型验证和评估指标.
主要成果:
- 多组和临床数据的整合提高了对生命状态,无转移存活率和无进展存活率的预测.
- 讨论处理受审查的患者数据的方法.
- 评估各种数据整合和预测技术的有效性.
结论:
- 综合多组和临床数据为癌症存活率预测提供了更强大的方法.
- 需要进一步开发以应对生存预测方法学的突出挑战.
- 本综述为了解和应用瘤学的先进预后模型提供了一个全面的资源.
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