Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data.

ABSTRACT: Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined o

SUBMITTER: Sidorczuk K 

PROVIDER: S-EPMC9487607 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature