AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.

ABSTRACT: Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation l

SUBMITTER: Charoenkwan P 

PROVIDER: S-EPMC9095707 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature