Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model

Abstract Background Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of an...

Full description

Bibliographic Details
Main Authors: Shahid Akbar, Ali Raza, Quan Zou
Format: Article
Language:English
Published: BMC 2024-03-01
Series:BMC Bioinformatics
Subjects:
Antiviral peptides
Prediction
Tri-segmentation based evolutionary features
Word embedding
Feature selection
Stacked ensemble model
Online Access:https://doi.org/10.1186/s12859-024-05726-5

Internet

https://doi.org/10.1186/s12859-024-05726-5

Similar Items