AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning.

ABSTRACT:

Background

Deep learning's automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of Hemolytic Activity Prediction of Antimicrobial Peptides creates a challenge due to the small amount of available data.

Results

Three different datasets for hemolysis activity prediction of therapeutic and antimicrobial peptides are gathered and the AMPDeep pipeline is implemented for each. The result demonstrate that AMPDeep outperforms the previous works on all three datasets, including works that use physicochemical features to represent the peptides or those who solely rely on the sequence and use deep learning to learn representation fo

SUBMITTER: Salem M 

PROVIDER: S-EPMC9511757 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature