Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.
Essential genes are those whose presence is vital for a cell's survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically modified microorganisms, and identifying targets...
Saved in:
| Main Authors: | Itunuoluwa Isewon, Stephen Binaansim, Faith Adegoke, Jerry Emmanuel, Jelili Oyelade |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315530 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network
by: Itunuoluwa Isewon, et al.
Published: (2024-01-01) -
An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction
by: Jerry Emmanuel, et al.
Published: (2025-01-01) -
Mitochondrial ATP synthesis is essential for efficient gametogenesis in Plasmodium falciparum
by: Penny C. Sparkes, et al.
Published: (2024-11-01) -
Inhibitors of Protein Targets of Plasmodium falciparum
by: Solomon Uche Oranusi, et al.
Published: (2024-12-01) -
Trafficked Proteins—Druggable in Plasmodium falciparum?
by: Jasmin Lindner, et al.
Published: (2013-01-01)