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HyperTraPS-CT:用灵活的数据和模型结构推断和预测积累路径

Olav N L Aga^1,2, Morten Brun³, Kazeem A Dauda³

¹Computational Biology Unit, University of Bergen, Bergen, Norway.

PLoS computational biology

|September 4, 2024

概括

我们开发了HyperTraPS-CT来建模复杂的生物积累过程,在连续时间内有许多相互作用的特征. 这种方法准确地从各种数据中捕获特征动态,从而能够更好地预测癌症和疾病进展.

科学领域:

计算生物学计算生物学
系统生物学系统生物学
进化生物学进化生物学

背景情况:

积累过程在生物学中至关重要,从进化到疾病.
当前的方法难以处理复杂的,多特征的交互和各种数据类型.

研究的目的:

介绍HyperTraPS-CT,用于许多合特征的连续时间动态.
处理不受限制的状态空间和不确定的观察时间.

主要方法:

超立方过渡路径在连续时间采样 (HyperTraPS-CT).
适应横截面,纵向和遗传学数据,采样时间灵活.
支持贝叶斯式和最大概率推理对任意特征相互作用.

主要成果:

识别特征子集之间的正负相互作用.
能够预测未来和未观察到的特征.
为功能交互提供可视化和模型选择.

结论:

HyperTraPS-CT提供了一种灵活而强大的方法来建模复杂的生物积累过程.
在癌症突变和抗菌素耐药性研究中显示出有用性.
方便准确的预测,与应用研究重点保持一致.

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