ArticleScientific reports2016
Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Cross-kingdom pathogen detection via duplex universal PCR and high-resolution melt.Biosensors & bioelectronics · 2025Article
- Universal digital high-resolution melting for the detection of pulmonary mold infections.Journal of clinical microbiology · 2024Article
- Machine learning based DNA melt curve profiling enables automated novel genotype detection.BMC bioinformatics · 2024Article
- Universal Digital High-Resolution Melt Analysis for the Diagnosis of Bacteremia.The Journal of molecular diagnostics : JMD · 2024Article
- Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration.Analytica chimica acta · 2024Article
- Decoding human cytomegalovirus for the development of innovative diagnostics to detect congenital infection.Pediatric research · 2024Review
- Harnessing Variabilities in Digital Melt Curves for Accurate Identification of Bacteria.Analytical chemistry · 2023Article
- Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing.Antibiotics (Basel, Switzerland) · 2022Review
- Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic.Environmental science and pollution research international · 2021Review
- Development of DNA Melt Curve Analysis for the Identification of Lepidopteran Pests in Almonds and Pistachios.Insects · 2021Article
- Efficacy of 16S rRNA variable regions high-resolution melt analysis for bacterial pathogens identification in periprosthetic joint infections.BMC microbiology · 2021Article
- Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting.Journal of clinical microbiology · 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
- Fast and Reliable Differentiation of Eight Trichinella Species Using a High Resolution Melting Assay.Scientific reports · 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
- Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification.PloS one · 2017Article
- A 'culture' shift: Application of molecular techniques for diagnosing polymicrobial infections.Biotechnology advancesReview
- An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections.Expert review of molecular diagnosticsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discriminating power of HRM + SVM for a single genetic locus by testing longer amplicons harboring significantly more sequence information. Using universal primers that amplify the hypervariable bacterial 16 S rRNA gene as a model system, we found that long amplicons yield more complex HRM curve shapes. We developed a novel nested OVO SVM approach to take advantage of this feature and achieved 100% accuracy in the identification of 37 clinically relevant bacteria in Leave-One-Out-Cross-Validation. A subset of organisms were independently tested. Those from pure culture were identified with high accuracy, while those tested directly from clinical blood bottles displayed more technical variability and reduced accuracy. Our findings demonstrate that long sequences can be accurately and automatically profiled by HRM with a novel nested SVM approach and suggest that clinical sample testing is feasible with further optimization.
Indexed as
Identifiers
What OpenQuestion holds
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.