ArticleGigaScience2020
Artificial intelligence deciphers codes for color and odor perceptions based on large-scale chemoinformatic data.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Construction of a machine learning-based predictive model for odor-driven behavioral preference of odorant molecules.Current research in food science · 2026Article
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images.Heliyon · 2024Article
- Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures.Molecules (Basel, Switzerland) · 2023Article
- OWSum: algorithmic odor prediction and insight into structure-odor relationships.Journal of cheminformatics · 2023Article
- Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning.Foods (Basel, Switzerland) · 2022Article
- Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15-year follow-up study.Cancer communications (London, England) · 2022Article
- Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence.Frontiers in neurorobotics · 2022Article
- Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study.JMIR medical informatics · 2021Article
- SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.International journal of molecular sciences · 2021Article
- Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning.Frontiers in medicine · 2021Article
- Artificial intelligence deciphers codes for color and odor perceptions based on large-scale chemoinformatic data.GigaScience · 2020Article
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Authors and funding
16 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundColor vision is the ability to detect, distinguish, and analyze the wavelength distributions of light independent of the total intensity. It mediates the interaction between an organism and its environment from multiple important aspects. However, the physicochemical basis of color coding has not been explored completely, and how color perception is integrated with other sensory input, typically odor, is unclear.
resultsHere, we developed an artificial intelligence platform to train algorithms for distinguishing color and odor based on the large-scale physicochemical features of 1,267 and 598 structurally diverse molecules, respectively. The predictive accuracies achieved using the random forest and deep belief network for the prediction of color were 100% and 95.23% ± 0.40% (mean ± SD), respectively. The predictive accuracies achieved using the random forest and deep belief network for the prediction of odor were 93.40% ± 0.31% and 94.75% ± 0.44% (mean ± SD), respectively. Twenty-four physicochemical features were sufficient for the accurate prediction of color, while 39 physicochemical features were sufficient for the accurate prediction of odor. A positive correlation between the color-coding and odor-coding properties of the molecules was predicted. A group of descriptors was found to interlink prominently in color and odor perceptions.
conclusionsOur random forest model and deep belief network accurately predicted the colors and odors of structurally diverse molecules. These findings extend our understanding of the molecular and structural basis of color vision and reveal the interrelationship between color and odor perceptions in nature.
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