Unsupervised encoding selection through ensemble pruning for biomedical classification.

ABSTRACT:

Background

Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers leverage artificial intelligence to circumvent the cumbersome, wet-lab-based identification and automate the detection of promising candidates. However, the prediction of a peptide's function is not limited to antimicrobial efficiency. To date, multiple studies successfully classified additional properties, e.g., antiviral or cell-penetrating effects. In this light, ensemble classifiers are employed aiming to further improve the prediction. Although we recently presented a workflow to significan

SUBMITTER: Spanig S 

PROVIDER: S-EPMC10018861 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature