ArticlePloS one2014
Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- High resolution melting assay as a reliable method for diagnosing drug-resistant TB cases: a systematic review and meta-analysis.BMC infectious diseases · 2021Pooled it
- Application of Universal LNA Probe-Mediated Multiplex Real-Time PCR for the Detection of Antibiotic Resistance Genes.Molecular biotechnology · 2026Article
- When DNA Tells the Tale: High-Resolution Melting as a Forensic Tool for Mediterranean Cetacean Identification.International journal of molecular sciences · 2025Article
- A Multiplex High-Resolution Melting (HRM) assay to differentiate Fusarium graminearum chemotypes.Scientific reports · 2024Article
- Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.Analytical chemistry · 2024Article
- Machine learning based DNA melt curve profiling enables automated novel genotype detection.BMC bioinformatics · 2024Article
- Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration.Analytica chimica acta · 2024Article
- Harnessing Variabilities in Digital Melt Curves for Accurate Identification of Bacteria.Analytical chemistry · 2023Article
- Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR.Trends in analytical chemistry : TRAC · 2023Article
- Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends.Analytical chemistry · 2022Article
- Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing.Antibiotics (Basel, Switzerland) · 2022Review
- Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates.Frontiers in molecular biosciences · 2021Article
- Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.World journal of microbiology & biotechnology · 2020Article
- Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting.Journal of clinical microbiology · 2020Article
- Three-gene PCR and high-resolution melting analysis for differentiating vertebrate species mitochondrial DNA for biodiversity research and complementing forensic surveillance.Scientific reports · 2020Article
- Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test.Analytical chemistry · 2019Article
- Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing.Annual review of analytical chemistry (Palo Alto, Calif.) · 2019Review
- High-Resolution Melting Analysis for Rapid Detection of Sequence Type 131 Escherichia coli.Antimicrobial agents and chemotherapy · 2017Article
- Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling.Scientific reports · 2017Article
- Microbial Typing by Machine Learned DNA Melt Signatures.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new method for automated HRM curve classification based on machine learning methods and learned tolerance for reaction condition deviations. We tested this method in silico through multiple cross-validations using curves generated from 9 different simulated experimental conditions to classify 92 known serotypes of Streptococcus pneumoniae and demonstrated over 99% accuracy with 8 training curves per serotype. In vitro verification of the algorithm was tested using sequence variants of a cancer-related gene and demonstrated 100% accuracy with 3 training curves per sequence variant. The machine learning algorithm enabled reliable, scalable, and automated HRM genotyping analysis with broad potential clinical and epidemiological applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.