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- W2898511110 endingPage "22" @default.
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- W2898511110 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." @default.
- W2898511110 created "2018-11-02" @default.
- W2898511110 creator A5002244679 @default.
- W2898511110 creator A5017400006 @default.
- W2898511110 creator A5079793229 @default.
- W2898511110 date "2018-10-27" @default.
- W2898511110 modified "2023-10-17" @default.
- W2898511110 title "Machine Learning in Human Olfactory Research" @default.
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