AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Sep 2, 2024
Abstract
MOTIVATION: Automated machine learning (AutoML) solutions can bridge the gap between new computational advances and their real-world applications by enabling experimental scientists to build their own custom models. We examine different steps in the development life-cycle of peptide bioactivity binary predictors and identify key steps where automation cannot only result in a more accessible method, but also more robust and interpretable evaluation leading to more trustworthy models.