预测生物医学中的多模式分析方法
Arber Qoku1,2,3, Nikoletta Katsaouni4,5,6, Nadine Flinner7,8,9,10
1German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Germany, Frankfurt am Main, Germany.
Computational and structural biotechnology journal
|December 13, 2023
概括
计算工具对于个性化医疗至关重要,分析复杂的患者数据. 本综述探讨了整合各种数据类型的方法,如omics和成像,以推进预测生物医学研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 统计遗传学 统计遗传学
背景情况:
- 个性化医疗需要先进的计算和统计工具来解释复杂的患者数据.
- 分析各种数据类型对于理解疾病生物学和增强预测建模至关重要.
研究的目的:
- 审查最近的计算和统计方法来分析预测生物医学中的多式联络患者数据.
- 突出整合各种数据类型的方法,包括omics,成像和基因组变异.
主要方法:
- 对多式联运数据分析的最新方法的文献综述.
- 专注于将不同的OMIC测量与成像或基因组变异数据相结合的方法.
主要成果:
- 在预测生物医学中分析多模式数据是一个快速发展的领域.
- 为了应对整合异质患者数据的挑战,存在各种各样的方法.
- 目前的方法显示,有望在预测建模和分子理解方面取得新的发展.
结论:
- 开发强大的计算和统计工具对于实现个性化医学的潜力至关重要.
- 对多式联运数据集成方法的进一步研究将推动疾病的理解和治疗的进步.
- 审查的方法为预测性生物医学研究的未来创新提供了基础.
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