ArticleMethods in molecular biology (Clifton, N.J.)2025
Predictive Machine Learning Models for Olfaction.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Editorial: Veterinary rhinology and emerging insights in olfaction.Frontiers in allergy · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A classical problem in neuroscience, biology, and chemistry is linking the chemical structure of odorants to their olfactory perception. This difficulty arises from the subjective nature of odor perception, incomplete understanding of the physiological mechanisms involved, and the absence of standardized odor descriptions. Machine learning and other computational approaches have recently been applied to tackle this challenge. This chapter presents a comprehensive workflow for constructing machine learning models for odor prediction, covering everything from problem formulation to model evaluation and real-world deployment. We also delve into recent advancements to enhance and interpret data-driven predictions while acknowledging the current limitations. The methodology outlined here offers a valuable framework for synthetic chemists and data scientists, enabling them to address the broader issue of olfaction and cater to specific needs within the fragrance and perfume industries.
Indexed as
Identifiers
40249484What 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.