Joel D. Mainland

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

The paper presents an ensemble model that improves predictions of perceptual similarity between odor mixtures using a curated dataset and collaborative challenge results.

Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception

The paper shows that odor mixtures can be predicted linearly from component odorant profiles, challenging the assumption of nonlinear interactions.

Odor Sampling Bags Enable Reliable Delivery of Controlled Odor Concentrations

The paper introduces an odor sampling bag system that improves odorant concentration control and reliability compared to traditional methods.

Mice and humans evaluate odor stimulus strength using common psychophysical principles

This study shows mice and humans use a common perceptual scale to assess odor intensity, demonstrating shared principles in stimulus evaluation.

A quantitative framework for predicting odor intensity across molecule and mixtures

This study develops a deep learning framework to predict odor intensity from physical properties of molecules and mixtures, overcoming limitations of traditional methods.

A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

The paper develops a Principal Odor Map using graph neural networks that matches human-level odor description performance and generalizes to diverse odor prediction tasks.

Pyrfume: A Window to the World's Olfactory Data

Pyrfume provides a unified framework to access and analyze diverse olfactory datasets across species and modalities, enabling novel analyses and benchmarks for machine olfaction.

Drawing the Borders of Olfactory Space

The paper develops a model to predict odorous molecules based on chemical properties and estimates the number of possible odorous compounds to be over 30 billion.

A 3D transcriptomics atlas of the mouse olfactory mucosa

This paper creates a 3D spatial transcriptomics atlas of the mouse olfactory mucosa, revealing spatial gene distribution patterns and supporting the chromatographic theory of olfaction.

From musk to body odor: decoding olfaction through genetic variation

This study identifies genetic associations between olfactory receptors and odor perception in humans, revealing novel links and supporting the hypothesis of olfactory gene degradation.

Deconstructing the mouse olfactory percept through an olfactory ethological atlas

This study creates a comprehensive mouse olfactory ethological atlas using behavioral responses to 73 odorants, revealing shared properties between mouse and human olfactory perception.

A transcriptomic atlas of mammalian olfactory mucosae reveals an evolutionary influence on food odor detection in humans

This study creates a transcriptomic atlas of olfactory mucosae across species to show evolutionary patterns in odor detection capabilities.

Sensory nutrition: The role of taste in the reviews of commercial food products

The paper analyzes consumer reviews to show that taste is the most frequently mentioned factor in food product ratings, with specific taste qualities like sweet and bitter being particularly common.

Genetic variation across the human olfactory receptor repertoire alters odor perception

The paper investigates how genetic variation in olfactory receptors affects odor perception by analyzing sequencing data and phenotypes from 332 individuals.

A competitive binding model predicts nonlinear responses of olfactory receptors to complex mixtures

The paper develops a competitive binding model to predict olfactory receptor responses to complex odorant mixtures, achieving 15% accuracy using single-odorant responses.

Reverse-engineering human olfactory perception from chemical features of odor molecules

The paper demonstrates that machine learning can accurately predict odor perception from molecular features, enabling reverse-engineering of smell.