Learning dynamics from large biological data sets: Machine learning meets systems biology
Learning dynamics from large biological data sets: Machine learning meets systems biology
复制标题
DOI：
10.1016/j.coisb.2020.07.009
复制
发表时间：
2020-08-01
影响因子：
3.7
通讯作者：
Forger, Daniel B.
中科院分区：
文献类型：
--
作者：
Gilpin, William;Huang, Yitong;Forger, Daniel B.
在过去的几十年里，基于动力系统理论的数学模型为不同的生物系统提供了新的见解。在这篇综述中，我们要问的是，机器学习技术最近在大规模生物数据分析中的成功是否为更传统的建模方法提供了一种补充或竞争的方法。最近机器学习在不同系统中学习生物动力学问题的应用范围从神经科学到动物行为。我们比较了传统动态模型与机器学习模型的基本机制和局限性。我们强调了传统建模在为生物系统提供预测性见解方面所发挥的独特作用，并提出了几种途径，用于将传统动力系统理论与机器学习实现的大规模分析相结合。
In the past few decades, mathematical models based on dynamical systems theory have provided new insight into diverse biological systems. In this review, we ask whether the recent success of machine learning techniques for large-scale biological data analysis provides a complementary or competing approach to more traditional modeling approaches. Recent applications of machine learning to the problem of learning biological dynamics in diverse systems range from neuroscience to animal behavior. We compare the underlying mechanisms and limitations of traditional dynamical models with those of machine learning models. We highlight the unique role that traditional modeling has played in providing predictive insights into biological systems, and we propose several avenues for bridging traditional dynamical systems theory with large-scale analysis enabled by machine learning.