Machine learning in human olfactory research.

ABSTRACT: 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 (i) the study of the physiology of pattern-based odor detection and recognition processes, (ii) pattern recognition in olfactory phenotypes, (iii) the development of complex disease biomarkers including olfactory features, (iv) odor prediction from physicochemical properties of volatile molecules and (v) 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.

SUBMITTER: Lotsch J 

PROVIDER: S-EPMC6295796 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other