The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and recognition processes, 2) pattern recognition in olfactory phenotypes, 3) the development of complex disease biomarkers including olfactory features, 4) odor prediction from physico-chemical properties of volatile molecules, and 5) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.

中文翻译：

---

人类嗅觉研究中的机器学习。


人类嗅觉的复杂性越来越多地反映在复杂和高维的数据中，这为补充假设驱动研究的数据驱动方法提供了机会。计算和数据科学的当代发展，及其目前最流行的机器学习实现，促进了复杂的数据驱动的研究方法。机器学习在人类嗅觉研究中的应用包括以下主要方法：1）基于模式的气味检测和识别过程的生理学研究，2）嗅觉表型的模式识别，3）复杂疾病生物标志物的开发，包括嗅觉特征， 4）根据挥发性分子的物理化学性质进行气味预测，5）公共大数据库中的知识发现。这些项目中使用了一组有限的无监督和监督机器学习方法，然而，越来越多的报告采用机器学习进行人类嗅觉研究，这反映出当代计算科学方法的日益使用。这篇综述提供了机器学习的关键概念，并总结了人类嗅觉数据的当前应用。