A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification.

ABSTRACT: Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, several adaptive statistical techniques have been devised. Despite significant gains, prediction performance is still constrained by the lack of appropriate feature descriptors and learning strategies in current systems. Moreover, good ground truth data is important for Artificial Intelligence (AI)-based models but there is a lack of that data in the literature. Therefore, in this work, we propose a novel hybrid network that combines 1D Convolutional Neural Network (CNN) and Bidirectional Gated Re

SUBMITTER: Alsanousi WA 

PROVIDER: S-EPMC9536844 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature