UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.

ABSTRACT: Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely rand

SUBMITTER: Charoenkwan P 

PROVIDER: S-EPMC8658322 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature