ArticleBMC medical genomics2020
Machine learning based refined differential gene expression analysis of pediatric sepsis.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- TENTACLES: a consensus machine learning tool for robust biomarker discovery in heterogeneous data.BioData mining · 2026Article
- Integrative machine learning reveals hidden and emerging co-regulatory gene networks for multi-phase glioblastoma outcome prediction.European journal of cancer (Oxford, England : 1990) · 2026Article
- Hybrid Feature Selection-Based Machine Learning and Deep Learning Framework for Biomarker Prediction From RNA-seq Data During Dengue Fever to Severe Dengue Progression.Evolutionary bioinformatics online · 2026Article
- Drug Discovery from Gene Expression/Gene Regulation.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Multi-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis.Frontiers in pharmacology · 2026Review
- ACmix-Swin Deep Learning of 4-Day-OldGenes · 2025Article
- Transcriptomic Profiling Reveals Distinct Immune Dysregulation in Early-Stage Sepsis Patients.International journal of molecular sciences · 2025Article
- Machine learning reveals distinct gene expression signatures across tissue states in stony coral tissue loss disease.Royal Society open science · 2025Article
- A machine learning-based investigation of integrin expression patterns in cancer and metastasis.Scientific reports · 2025Article
- Integrated analysis of N-glycosylation and Alzheimer's disease: identifying key biomarkers and mechanisms.Frontiers in aging neuroscience · 2025Article
- Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.Frontiers in immunology · 2025Article
- Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.Briefings in bioinformatics · 2024Article
- Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.Scientific reports · 2024Article
- Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.NAR genomics and bioinformatics · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- Transcriptional markers classifying Escherichia coli and Staphylococcus aureus induced sepsis in adults: A data-driven approach.PloS one · 2024Article
- Early detection of abiotic stress in plants through SNARE proteins using hybrid feature fusion model.PeerJ. Computer science · 2024Article
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- N-of-one differential gene expression without control samples using a deep generative model.Genome biology · 2023Article
- Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets.Archives of toxicology · 2023Article
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2 authors.
Funding
Abstract
backgroundDifferential 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.
methodsIn 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.
resultsUsing 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.
conclusionsMachine 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.
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