ArticleScientific reports2021
Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Development of a sensitive disease-screening model using comprehensive circulating microRNA profiles in dogs: A pilot study.Veterinary and animal science · 2025Article
- AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania's First Wave of COVID-19.Bioengineering (Basel, Switzerland) · 2024Article
- A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.Translational psychiatry · 2024Article
- Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.Diagnostics (Basel, Switzerland) · 2024Article
- Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.Frontiers in digital health · 2024Review
- Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage.PloS one · 2024Article
- Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic.Current transplantation reports · 2023Article
- A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.IBRO neuroscience reports · 2023Article
- Automated machine learning for genome wide association studies.Bioinformatics (Oxford, England) · 2023Article
- Comprehensive circulating microRNA profile as a supersensitive biomarker for early-stage lung cancer screening.Journal of cancer research and clinical oncology · 2023Article
- Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results.Scientific reports · 2023Article
- Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach.Children (Basel, Switzerland) · 2023Article
- Rapid Detection of SARS-CoV-2 Variants of Concern by Genomic Surveillance Techniques.Advances in experimental medicine and biology · 2023Article
- Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework.Computational intelligence and neuroscience · 2023Article
- Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023Article
- A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity.Scientific reports · 2022Article
- Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.Scientific reports · 2022Article
- Molecular signature of postmortem lung tissue from COVID-19 patients suggests distinct trajectories driving mortality.Disease models & mechanisms · 2022Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723-0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865-0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899-0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.
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