AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors.

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.

Results

We present a new automated method for drawing negative peptides that achieves better balance between specificity and generalization than current alternatives. We study the effect of homology-based partitioning for generating the training and testing data subsets and demonstrate that model

SUBMITTER: Fernandez-Diaz R 

PROVIDER: S-EPMC11438549 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature