ArticleScientific reports2020
Data based predictive models for odor perception.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- PharmaGNN: a model for odor prediction based on graph neural networks.Journal of the science of food and agriculture · 2026Article
- Odor-Sight: An Interpretable Graph Neural Network Web Platform for Prediction of Odorant Activity.Journal of chemical information and modeling · 2026Article
- Hierarchies of smell: structuring the molecular odor space using semantic taxonomies and machine learning.Chemical senses · 2026Article
- What makes a scent trigger a memory? A cognitive decomposition of odor-evoked retrieval.iScience · 2026Article
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- Machine Learning in Sensory Analysis of Mead-A Case Study: Ensembles of Classifiers.Molecules (Basel, Switzerland) · 2025Article
- Predictive Machine Learning Models for Olfaction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Automatic scent creation by cheminformatics method.Scientific reports · 2024Article
- Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu.iScience · 2024Article
- Deep active learning with high structural discriminability for molecular mutagenicity prediction.Communications biology · 2024Article
- Article
- A BERT-based pretraining model for extracting molecular structural information from a SMILES sequence.Journal of cheminformatics · 2024Article
- Volatile Composition of Fortification Grape Spirit and Port Wine: Where Do We Stand?Foods (Basel, Switzerland) · 2023Review
- Data-Driven Elucidation of Flavor Chemistry.Journal of agricultural and food chemistry · 2023Review
- OWSum: algorithmic odor prediction and insight into structure-odor relationships.Journal of cheminformatics · 2023Article
- Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour.PloS one · 2023Article
- Investigation of the structure-odor relationship using a Transformer model.Journal of cheminformatics · 2022Article
- Synthesis of Cyclic Fragrances via Transformations of Alkenes, Alkynes and Enynes: Strategies and Recent Progress.Molecules (Basel, Switzerland) · 2022Review
- Predicting individual perceptual scent impression from imbalanced dataset using mass spectrum of odorant molecules.Scientific reports · 2022Article
- Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges.Frontiers in systems neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of "sweet" and "musky". We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.
Indexed as
Identifiers
What OpenQuestion holds
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.