Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.

ABSTRACT: Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. There

SUBMITTER: Liang X 

PROVIDER: S-EPMC8294543 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature