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Systems Approaches in Molecular and Cell Biology: Making Sense out of Data; Providing Meaning to Models

机译:分子和细胞生物学中的系统方法:从数据中获取意义;为模型提供意义

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摘要

Two very different research strategies and mathematical modeling cultures will be outlined: Bottom-up theory-driven modeling and top-down data-driven modeling. The former encapsulates prior knowledge and hypotheses, while the latter extracts relevant co-variation patterns within and between large data tables. The former relies on the scientist defining non-linear differential equations to model the dynamics of a process; the latter automatically finds and displays dominant latent structures by multivariate eigen-structure approximation. The two approaches have different strengths and weaknesses. Based on our experiences, we therefore here suggest a possible way for combining these approaches.
机译:将概述两种截然不同的研究策略和数学建模文化:自下而上的理论驱动的建模和自上而下的数据驱动的建模。前者封装了先验知识和假设,而后者则提取了大型数据表内部和之间的相关协变模式。前者依靠科学家定义非线性微分方程来对过程动力学建模。后者通过多元特征结构近似自动找到并显示主要的潜在结构。两种方法各有优缺点。因此,根据我们的经验,我们在这里提出一种组合这些方法的可能方法。

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