Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.
Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.
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DOI：
10.3389/fbinf.2021.746712
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lecca, Paola
中科院分区：
文献类型：
--
作者：
Lecca, Paola
关键词：
大多数基于机器学习的方法预测结果，而不是理解因果关系。机器学习方法已被证明在发现数据中的相关性方面是有效的，但在确定因果关系方面却不熟练。这个问题严重限制了机器学习方法推断生物网络实体之间因果关系的适用性，更一般地说，任何动态系统，如医疗干预策略和临床结果系统，都可以表示为网络。从那些希望使用网络推理结果的人的角度来看，不仅要了解动态背后的机制，还要了解网络如何对外部刺激做出反应。G.环境因素、治疗方法），非常需要能够理解数据之间因果关系的工具。鉴于机器学习技术在计算生物学中的日益普及，以及最近提出使用机器学习技术来推断生物网络的文献，我们想提出数学和计算机科学研究在将机器学习推广到能够理解因果关系的方法方面所面临的挑战，而实现这一点将为系统生物学的医学应用领域开辟前景，系统生物学的主要范式正是任何物理尺度上的网络生物学。
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.
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