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Chem Senses

Title:		Machine Learning in Human Olfactory Research
Author(s):		Lotsch J; Kringel D; Hummel T;
Address:		"Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany. Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Frankfurt am Main, Germany. Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Dresden, Germany"
Journal Title:		Chem Senses
Year:		2019
Volume:		44
Issue:		1
Page Number:		11 - 22
DOI:		10.1093/chemse/bjy067
ISSN/ISBN:		1464-3553 (Electronic) 0379-864X (Print) 0379-864X (Linking)
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 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"
Keywords:		"Biomarkers/analysis Databases, Factual Electronic Nose Humans *Machine Learning Odorants/*analysis Smell/*physiology Volatile Organic Compounds/chemistry;"
Notes:		"MedlineLotsch, Jorn Kringel, Dario Hummel, Thomas eng Research Support, Non-U.S. Gov't Review England 2018/10/30 Chem Senses. 2019 Jan 1; 44(1):11-22. doi: 10.1093/chemse/bjy067"