Machine learning based refined differential gene expression analysis of pediatric sepsis
Abstract Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups....
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| Format: | Article |
| Language: | English |
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BMC
2020-08-01
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| Series: | BMC Medical Genomics |
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| Online Access: | http://link.springer.com/article/10.1186/s12920-020-00771-4 |
| _version_ | 1818656758634119168 |
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| author | Mostafa Abbas Yasser EL-Manzalawy |
| author_facet | Mostafa Abbas Yasser EL-Manzalawy |
| author_sort | Mostafa Abbas |
| collection | DOAJ |
| description | Abstract Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. Methods In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Results Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Conclusions Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis. |
| first_indexed | 2024-12-17T03:30:41Z |
| format | Article |
| id | doaj.art-d33d8aefa4b0405c8072a2fde5554fc7 |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-17T03:30:41Z |
| publishDate | 2020-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-d33d8aefa4b0405c8072a2fde5554fc72022-12-21T22:05:16ZengBMCBMC Medical Genomics1755-87942020-08-0113111010.1186/s12920-020-00771-4Machine learning based refined differential gene expression analysis of pediatric sepsisMostafa Abbas0Yasser EL-Manzalawy1Department of Imaging Science and Innovation, Geisinger Health SystemDepartment of Imaging Science and Innovation, Geisinger Health SystemAbstract Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. Methods In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Results Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Conclusions Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.http://link.springer.com/article/10.1186/s12920-020-00771-4Biomarkers discoveryDifferential expression analysisRefined differential gene expression analysisFeature selection |
| spellingShingle | Mostafa Abbas Yasser EL-Manzalawy Machine learning based refined differential gene expression analysis of pediatric sepsis BMC Medical Genomics Biomarkers discovery Differential expression analysis Refined differential gene expression analysis Feature selection |
| title | Machine learning based refined differential gene expression analysis of pediatric sepsis |
| title_full | Machine learning based refined differential gene expression analysis of pediatric sepsis |
| title_fullStr | Machine learning based refined differential gene expression analysis of pediatric sepsis |
| title_full_unstemmed | Machine learning based refined differential gene expression analysis of pediatric sepsis |
| title_short | Machine learning based refined differential gene expression analysis of pediatric sepsis |
| title_sort | machine learning based refined differential gene expression analysis of pediatric sepsis |
| topic | Biomarkers discovery Differential expression analysis Refined differential gene expression analysis Feature selection |
| url | http://link.springer.com/article/10.1186/s12920-020-00771-4 |
| work_keys_str_mv | AT mostafaabbas machinelearningbasedrefineddifferentialgeneexpressionanalysisofpediatricsepsis AT yasserelmanzalawy machinelearningbasedrefineddifferentialgeneexpressionanalysisofpediatricsepsis |