FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.

ABSTRACT: Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random For

SUBMITTER: Herrera-Rocha F 

PROVIDER: S-EPMC11633011 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature