CrossLabFit: A Novel Framework for Integrating Qualitative and Quantitative Data Across Multiple Labs for Model Calibration.

Rodolfo Blanco-Rodriguez, Tanya A Miura, Esteban Hernandez-Vargas
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Abstract

The integration of computational models with experimental data is a cornerstone for gaining insight into biomedical applications. However, parameter fitting procedures often require a vast availability and frequency of data that are challenging to obtain from a single source. Here, we present a novel methodology "CrossLabFit" designed to integrate qualitative data from multiple laboratories, overcoming the constraints of single-lab data collection. Our approach harmonizes disparate qualitative assessments-ranging from different experimental labs to categorical observations-into a unified framework for parameter estimation. By using machine learning algorithms, these qualitative constraints are represented as dynamic "qualitative windows" that capture significant trends to which models must adhere. For numerical implementation, we developed a GPU-accelerated version of differential evolution to navigate in the cost function that integrated quantitative and qualitative data. We validate our approach across a series of case studies, demonstrating significant improvements in model accuracy and parameter identifiability. This work opens a new paradigm for collaborative science, enabling a methodological road to combine and compare findings between studies to improve our understanding of biological systems and beyond.

整合多个实验室的定性和定量数据进行模型校准。
计算模型与实验数据的集成是深入了解生物医学应用的基石。然而,参数拟合过程通常需要大量的可用性和数据频率,而这些数据很难从单一来源获得。在这里,我们提出了一种新的方法,旨在整合来自多个实验室的定性数据,克服了单一实验室数据收集的限制。我们的方法协调了不同的定性评估-从专家注释到分类观察-到参数估计的统一框架。这些定性约束被表示为动态的“定性窗口”,它捕获了模型必须遵循的重要趋势。对于数值实现,我们开发了一个gpu加速版本的差分进化,以在集成定量和定性数据的残差中导航。我们通过一系列案例研究验证了我们的方法,证明了模型准确性和参数可识别性的显着改进。这项工作为协作科学开辟了新的途径,使一种方法能够结合和比较研究之间的发现,以提高我们对生物系统的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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