ReviewChemical senses2019
Machine Learning in Human Olfactory Research.
Review in Chemical senses, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- Emotional Burden of Olfactory Dysfunction: A Psychological Cluster Analysis from a Tertiary Smell and Taste Clinic.International forum of allergy & rhinology · 2026Article
- Decoding Smell from Receptor Structure.Research square · 2026Article
- Hierarchies of smell: structuring the molecular odor space using semantic taxonomies and machine learning.Chemical senses · 2026Article
- Beyond olfaction: New insights into human odorant binding proteins.Protein science : a publication of the Protein Society · 2026Review
- What makes a scent trigger a memory? A cognitive decomposition of odor-evoked retrieval.iScience · 2026Article
- Olfactory ERP-based classification of anosmia and normosmia using machine learning.Clinical neurophysiology practice · 2026Article
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- A Structure-Based Approach for Predicting Odor Similarity of Molecules via Docking Simulations with Human Olfactory Receptors.ACS omega · 2025Article
- Advances in MEMS, Optical MEMS, and Nanophotonics Technologies for Volatile Organic Compound Detection and Applications.Small science · 2025Review
- Odor prediction of whiskies based on their molecular composition.Communications chemistry · 2024Article
- Classification of substances by health hazard using deep neural networks and molecular electron densities.Journal of cheminformatics · 2024Article
- Mlp4green: A Binary Classification Approach Specifically for Green Odor.International journal of molecular sciences · 2024Article
- Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform.Bioelectronic medicine · 2023Review
- Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19.Biosensors & bioelectronics · 2023Article
- OWSum: algorithmic odor prediction and insight into structure-odor relationships.Journal of cheminformatics · 2023Article
- Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis.Bioengineering (Basel, Switzerland) · 2023Article
- Unsupervised Clustering of Olfactory Phenotypes.American journal of rhinology & allergy · 2022Article
- Sorting of Odor Dilutions Is a Meaningful Addition to Assessments of Olfactory Function as Suggested by Machine-Learning-Based Analyses.Journal of clinical medicine · 2022Article
- Machine-Learning Points at Endoscopic, Quality of Life, and Olfactory Parameters as Outcome Criteria for Endoscopic Paranasal Sinus Surgery in Chronic Rhinosinusitis.Journal of clinical medicine · 2021Article
- Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose.The Journal of membrane biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.