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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315530 |
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| author | Itunuoluwa Isewon Stephen Binaansim Faith Adegoke Jerry Emmanuel Jelili Oyelade |
| author_facet | Itunuoluwa Isewon Stephen Binaansim Faith Adegoke Jerry Emmanuel Jelili Oyelade |
| author_sort | Itunuoluwa Isewon |
| collection | DOAJ |
| description | 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, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum, using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. The proposed approach substantially improved gene essentiality predictions as it considered the weighted and directed nature of metabolic networks and utilised network-based features, achieving a high accuracy rate of 0.85 and an AuROC of 0.7. Furthermore, this study enhanced the understanding of metabolic networks and their role in determining gene essentiality in Plasmodium falciparum. Notably, our model identified 9 genes previously considered non-essential in the Ogee database but now predicted to be essential, with some of them potentially serving as drug targets for malaria treatment, thereby opening exciting research avenues. |
| format | Article |
| id | doaj-art-820668b9b0f74fa0953343f65d5ca0f7 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-820668b9b0f74fa0953343f65d5ca0f72025-01-08T05:32:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031553010.1371/journal.pone.0315530Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.Itunuoluwa IsewonStephen BinaansimFaith AdegokeJerry EmmanuelJelili OyeladeEssential 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, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum, using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. The proposed approach substantially improved gene essentiality predictions as it considered the weighted and directed nature of metabolic networks and utilised network-based features, achieving a high accuracy rate of 0.85 and an AuROC of 0.7. Furthermore, this study enhanced the understanding of metabolic networks and their role in determining gene essentiality in Plasmodium falciparum. Notably, our model identified 9 genes previously considered non-essential in the Ogee database but now predicted to be essential, with some of them potentially serving as drug targets for malaria treatment, thereby opening exciting research avenues.https://doi.org/10.1371/journal.pone.0315530 |
| spellingShingle | Itunuoluwa Isewon Stephen Binaansim Faith Adegoke Jerry Emmanuel Jelili Oyelade Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. PLoS ONE |
| title | Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. |
| title_full | Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. |
| title_fullStr | Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. |
| title_full_unstemmed | Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. |
| title_short | Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. |
| title_sort | machine learning methods for predicting essential metabolic genes from plasmodium falciparum genome scale metabolic network |
| url | https://doi.org/10.1371/journal.pone.0315530 |
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