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.
Highlights
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures
By employing Automated Machine Learning (AutoML), the following challenges were addressed: Could we improve on the predictive power of the models? Can we reduce the number of serum factors needed to be measured without sacrificing performance to develop a cost-effective laboratory test? Can we obtain more accurate training estimates that better reflect the performance anticipated in a real-life setting? Most importantly, can AutoML improve on these aspects in a fully automated mode? To further elaborate on those answers, we analyzed datasets from two more studies addressing different COVID-19 related clinical questions[29,30], the latest used here for the first time for classification analysis, in an attempt to maximize predictive performance and accelerate the development of emerging diagnostics/prognostics
We used AutoML on the training cohort data of 13 severe and 18 non-severe COVID-19 patients provided by Shen et al.[9]
Summary
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. 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. AutoML automates algorithm selection, hyper-parameter tuning, performance estimation, and result visualization and interpretation In this way, AutoML tools promise to deliver reliable predictive and diagnostic models that can be interpretable to a non-expert, while drastically increasing the productivity of expert a nalysts[19]. To further elaborate on those answers, we analyzed datasets from two more studies addressing different COVID-19 related clinical questions[29,30], the latest used here for the first time for classification analysis, in an attempt to maximize predictive performance and accelerate the development of emerging diagnostics/prognostics.
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