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Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.
Vanderboom, Patrick M; Renuse, Santosh; Maus, Anthony D; Madugundu, Anil K; Kemp, Jennifer V; Gurtner, Kari M; Singh, Ravinder J; Grebe, Stefan K; Pandey, Akhilesh; Dasari, Surendra.
  • Vanderboom PM; Department of Laboratory Medicine and Pathology, Division of Clinical Biochemistry and Immunology, Mayo Clinic, Rochester, Minnesota 55905, United States.
  • Renuse S; Department of Laboratory Medicine and Pathology, Division of Clinical Biochemistry and Immunology, Mayo Clinic, Rochester, Minnesota 55905, United States.
  • Maus AD; Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.
  • Madugundu AK; Department of Laboratory Medicine and Pathology, Division of Clinical Biochemistry and Immunology, Mayo Clinic, Rochester, Minnesota 55905, United States.
  • Kemp JV; Department of Laboratory Medicine and Pathology, Division of Clinical Biochemistry and Immunology, Mayo Clinic, Rochester, Minnesota 55905, United States.
  • Gurtner KM; Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.
  • Singh RJ; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.
  • Grebe SK; Center for Molecular Medicine, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore 560029, Karnataka, India.
  • Pandey A; Department of Laboratory Medicine and Pathology, Division of Clinical Biochemistry and Immunology, Mayo Clinic, Rochester, Minnesota 55905, United States.
  • Dasari S; Department of Laboratory Medicine and Pathology, Division of Clinical Biochemistry and Immunology, Mayo Clinic, Rochester, Minnesota 55905, United States.
J Proteome Res ; 21(8): 2045-2054, 2022 08 05.
Article in English | MEDLINE | ID: covidwho-1947186
ABSTRACT
Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: J Proteome Res Journal subject: Biochemistry Year: 2022 Document Type: Article Affiliation country: Acs.jproteome.2c00156

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: J Proteome Res Journal subject: Biochemistry Year: 2022 Document Type: Article Affiliation country: Acs.jproteome.2c00156