MLACP 2.0: An updated machine learning tool for anticancer peptide prediction.

ABSTRACT: Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training d

SUBMITTER: Thi Phan L 

PROVIDER: S-EPMC9421197 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature