Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.
Hooman H RashidiJohn PepperTaylor HowardKarina KleinLarissa MaySamer AlbahraBrett PhinneyMichelle R SalemiNam K TranPublished in: PloS one (2022)
The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.
Keyphrases
- sars cov
- machine learning
- mass spectrometry
- high throughput
- deep learning
- liquid chromatography
- artificial intelligence
- respiratory syndrome coronavirus
- big data
- coronavirus disease
- capillary electrophoresis
- gas chromatography
- high performance liquid chromatography
- multiple sclerosis
- high resolution
- ms ms
- low cost
- loop mediated isothermal amplification
- copy number
- infectious diseases
- label free
- single molecule
- single cell
- simultaneous determination
- gene expression
- tandem mass spectrometry
- electronic health record
- sensitive detection
- quantum dots
- solid phase extraction