Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.

ABSTRACT: 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 for treatment. When essential genes are expressed, they give rise to essential proteins. Identifying these genes, especially in complex organisms like Plasmodium falciparum, which causes malaria, is challenging due to the cost and time associated with experimental methods. Thus, computational approaches have emerged. Early research in this area prioritised the study of less intricate organisms, inadvertently neglecting the complexities of metabolite transport in metabolic networks. To overcome this,

SUBMITTER: Isewon I 

PROVIDER: S-EPMC11666047 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature