Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa.

ABSTRACT:

Background

Apicomplexa consist of numerous pathogenic parasitic protistan genera that invade host cells and reside and replicate within the parasitophorous vacuole (PV). Through this interface, the parasite exchanges nutrients and affects transport and immune modulation. During the intracellular life-cycle, the specialized secretory organelles of the parasite secrete an array of proteins, among which dense granule proteins (GRAs) play a major role in the modification of the PV. Despite this important role of GRAs, a large number of potential GRAs remain unidentified in Apicomplexa.

Methods

A multi-view attention graph convolutional network (MVA-GCN) prediction model with multiple features was constructed using a combination of machine learning and genomic datasets, and the p

SUBMITTER: Lu Z 

PROVIDER: S-EPMC10012559 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature