SAMP: Identifying antimicrobial peptides by an ensemble learning model based on proportionalized split amino acid composition.

ABSTRACT: It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based comput

SUBMITTER: Feng J 

PROVIDER: S-EPMC11631067 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature