{"doi":"10.1371/journal.pone.0263954","title":"Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS","abstract":"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.","journal":"PLoS ONE","year":2022,"id":273000,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9537,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":940529,"name":"John V. Pepper","orcid":null,"position":1,"is_corresponding":false},{"id":940530,"name":"Taylor Howard","orcid":null,"position":2,"is_corresponding":false},{"id":939977,"name":"Karina Klein","orcid":"0000-0002-6878-5583","position":3,"is_corresponding":false},{"id":111257,"name":"Larissa May","orcid":null,"position":4,"is_corresponding":false},{"id":939978,"name":"Samer Albahra","orcid":"0000-0001-5184-747X","position":5,"is_corresponding":false},{"id":404184,"name":"Brett S. Phinney","orcid":"0000-0003-3870-3302","position":6,"is_corresponding":false},{"id":407513,"name":"Michelle Salemi","orcid":"0000-0003-3990-7964","position":7,"is_corresponding":false},{"id":557444,"name":"Nam K. Tran","orcid":"0000-0003-1565-0025","position":8,"is_corresponding":false},{"id":700284,"name":"Hooman H. Rashidi","orcid":"0000-0002-5634-8490","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:27:52.048702Z","pmid":"35905092","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}