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Machine Learning in Human Olfactory Research

 
: Lötsch, J.; Kringel, D.; Hummel, T.

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Fulltext ()

Chemical senses 44 (2019), No.1, pp.11-22
ISSN: 0379-864X
ISSN: 1464-3553
Deutsche Forschungsgemeinschaft DFG
HU 441/18-1
Deutsche Forschungsgemeinschaft DFG
Lo 612/10-1
European Commission EC
FP7-HEALTH; 602919; GLORIA
Understanding chronic pain and new druggable targets: Focus on glial-opioid receptor interface
English
Journal Article, Electronic Publication
Fraunhofer IME ()

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.

: http://publica.fraunhofer.de/documents/N-549498.html