Miyamoto Hirokuni, Kikuchi Jun
Graduate School of Horticulture, Chiba University, Matsudo, Chiba 271-8501, Japan.
RIKEN Center for Integrative Medical Science, Yokohama, Kanagawa 230-0045, Japan.
Comput Struct Biotechnol J. 2023 Jan 4;21:869-878. doi: 10.1016/j.csbj.2023.01.001. eCollection 2023.
The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.
自然界在不断变化，地球边界对生物多样性以及碳、氮、磷循环发出了严重警告。从其他角度来看，全球变暖、粮食短缺等社会问题正蔓延至各个领域。这些看似不相关的问题实则紧密相连，但可以说以综合的方式理解它们仍有一步之遥。然而，分析技术在各个领域都取得了进展，从微观角度来看，随着包括新一代测序仪（NGS）、核磁共振（NMR）、气相色谱 - 质谱联用仪（GC/MS）和液相色谱 - 质谱联用仪（LC/MS）等仪器的发展，可以获得各种形式的分子信息，如基因组数据、微生物群落结构、代谢组、蛋白质组和脂质组。新技术的发展使得获取各种形式的分子信息成为可能。从宏观角度来看，环境分析仪器以及卫星、无人机、观测船和半导体传感器等环境测量设施的发展增加了各种环境因素的数据可用性。基于这些背景，计算科学的作用是提供一种机制，用于整合和理解这些看似不同的数据集。本综述描述了机器学习以及解决这些问题所需的结构方程和这些数据的统计因果推断。除了介绍如何利用这些技术的实际例子外，我们还将讨论如何使用这些技术在社会中实施环境友好型技术。
