Machine learning to improve the interpretation of intercalating dye-based quantitative PCR results.

ABSTRACT: This study aimed to evaluate the contribution of Machine Learning (ML) approach in the interpretation of intercalating dye-based quantitative PCR (IDqPCR) signals applied to the diagnosis of mucormycosis. The ML-based classification approach was applied to 734 results of IDqPCR categorized as positive (n = 74) or negative (n = 660) for mucormycosis after combining "visual reading" of the amplification and denaturation curves with clinical, radiological and microbiological criteria. Fourteen features were calculated to characterize the curves and injected in several pipelines including four ML-algorithms. An initial subset (n = 345) was used for the conception of classifiers. The classifier predictions were combined with majority voting to estimate performances of 48 meta-classifiers on an

SUBMITTER: Godmer A 

PROVIDER: S-EPMC9525288 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature