杂交机理建模和深度学习,用于免疫检查点抑制剂免疫疗法后的个性化生存预测。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Joseph D Butner, Prashant Dogra, Caroline Chung, Eugene J Koay, James W Welsh, David S Hong, Vittorio Cristini, Zhihui Wang
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引用次数: 0

摘要

我们介绍了一项研究,该研究将预测性机理建模与深度学习方法相结合,预测免疫检查点抑制剂(ICI)免疫疗法下个体患者的生存概率。这种混合方法既能根据 ICI 治疗关键机制的机理模型计算出的指标(这些指标在临床上可能无法直接测量)进行预测,又能根据易于测量的数量或患者特征进行预测,而这些数量或特征并不总是很容易纳入预测性机理模型中。基于事件时间一致性、布赖尔评分和基于负二叉对数似然法的标准,在来自 93 名患者的机理+临床混合数据集上训练的深度学习时间到事件预测模型比仅在机理模型衍生值或仅在临床数据上训练的模型获得了更高的单个患者预测准确率。特征重要性分析表明,临床参数和模型衍生参数在提高预测准确率方面都发挥了重要作用,这进一步证明了我们的混合方法的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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