To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

中文翻译：

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机器学习及其在微生物学中的应用

要在分子水平上了解微生物的复杂性，需要了解大量数据，因此如果没有称为机器学习的人工智能应用程序，现在人类可能无法检测到有洞察力的数据模式。应用机器学习来解决生物学问题预计将以前所未有的速度增长，但在外行人看来，它是一个神秘而令人生畏的实体，委托给数学家和计算机科学家。本次审查的目的是确定开始成为一名有效的机器学习从业者之旅所需的关键点。这些关键点通过评估迄今为止如何在广泛的现实生活微生物学示例中应用机器学习得到进一步加强。这包括预测药物靶点或候选疫苗、诊断引起传染病的微生物、对抗微生物药物的耐药性分类、预测疾病爆发和探索微生物相互作用。我们希望激励微生物学家和其他相关研究人员加入新兴的机器学习革命。