Related Experiment Video
Updated: Aug 5, 2026
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ
Mihir R Atreya1, Shayantan Banerjee2, Andrew J Lautz1
1Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA.
Background:
Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on underlying biology and allow for accurate prediction of those at-risk.
Methods:
Secondary analyses of publicly available gene-expression datasets. Supervised machine learning (ML) was used to identify a parsimonious set of genes associated with a persistent MODS trajectory in a training set of pediatric septic shock. We optimized model parameters and tested risk-prediction capabilities in independent validation and test datasets, respectively. We compared model performance relative to an established gene-set predictive of sepsis mortality.
Findings:
Patients with a persistent MODS trajectory had 568 differentially expressed genes and characterized by a dysregulated innate immune response. Supervised ML identified 111 genes associated with the outcome of interest on repeated cross-validation, with an AUROC of 0.87 (95% CI: 0.85-0.88) in the training set. The optimized model, limited to 20 genes, achieved AUROCs ranging from 0.74 to 0.79 in the validation and test sets to predict those with persistent MODS, regardless of host age and cause of organ dysfunction. Our classifier demonstrated reproducibility in identifying those with persistent MODS in comparison with a published gene-set predictive of sepsis mortality.
Interpretation:
We demonstrate the utility of supervised ML driven identification of the genes associated with persistent MODS. Pending validation in enriched cohorts with a high burden of organ dysfunction, such an approach may inform targeted delivery of interventions among at-risk patients.
Funding:
H.R.W.'s NIHR35GM126943 award supported the work detailed in this manuscript. Upon his death, the award was transferred to M.N.A. M.R.A., N.S.P, and R.K were supported by NIHR21GM151703. R.K. was supported by R01GM139967.
Insights
Identifying genes linked to persistent Multiple Organ Dysfunction Syndrome (MODS) can improve early prediction and intervention for critically ill patients. Machine learning models show promise in pinpointing these critical genetic markers.
Area of Science:
- Genomics
- Critical Care Medicine
- Bioinformatics
Background:
- Multiple Organ Dysfunction Syndrome (MODS) is a major cause of death in critically ill patients, but its underlying biological mechanisms remain poorly understood.
- Identifying genes associated with persistent MODS trajectories is crucial for understanding its pathobiology and predicting patient risk.
Purpose of the Study:
- To identify a set of genes associated with a persistent MODS trajectory using supervised machine learning.
- To develop and validate a predictive model for persistent MODS in critically ill patients.
Main Methods:
- Secondary analysis of publicly available gene-expression datasets.
- Application of supervised machine learning (ML) to identify genes in pediatric septic shock patients.
- Optimization and validation of the ML model in independent datasets.
Main Results:
- A persistent MODS trajectory was characterized by 568 differentially expressed genes and a dysregulated innate immune response.
- Supervised ML identified 111 genes associated with persistent MODS, achieving an AUROC of 0.87 in the training set.
- An optimized 20-gene model predicted persistent MODS with AUROCs of 0.74-0.79 in validation and test sets, irrespective of host age or cause of organ dysfunction.
Conclusions:
- Supervised ML effectively identifies genes associated with persistent MODS.
- This approach holds potential for informing targeted interventions in at-risk populations, pending further validation in diverse cohorts.
More Related Videos
09:47Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Related Concept Videos
01:11Genetic Lingo
01:29Pharmacogenomics: Identification of New Drug Targets