DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

ABSTRACT: Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates str

SUBMITTER: Yao L 

PROVIDER: S-EPMC10503419 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature