ArticleJournal of biomedical science2009
HCV genotyping using statistical classification approach.
Article in Journal of biomedical science, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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
9 citing papers in PubMed.
- On leveraging self-supervised learning for accurate HCV genotyping.Scientific reports · 2024Article
- PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences.Biology · 2022Article
- A Single-step Multiplex Quantitative Real Time Polymerase Chain Reaction Assay for Hepatitis C Virus Genotypes.Journal of translational internal medicine · 2017Article
- HCV genotyping from NGS short reads and its application in genotype detection from HCV mixed infected plasma.PloS one · 2015Article
- Identification of novel small molecules as inhibitors of hepatitis C virus by structure-based virtual screening.International journal of molecular sciences · 2013Article
- Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray.BMC genomics · 2013Article
- Prevalence of hepatitis C virus genotypes in mashhad, northeast iran.Iranian journal of public health · 2012Article
- NS4A protein as a marker of HCV history suggests that different HCV genotypes originally evolved from genotype 1b.Virology journal · 2011Article
- Hepatitis C virus (HCV) genotype 1 subtype identification in new HCV drug development and future clinical practice.PloS one · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genotype of Hepatitis C Virus (HCV) strains is an important determinant of the severity and aggressiveness of liver infection as well as patient response to antiviral therapy. Fast and accurate determination of viral genotype could provide direction in the clinical management of patients with chronic HCV infections. Using publicly available HCV nucleotide sequences, we built a global Position Weight Matrix (PWM) for the HCV genome. Based on the PWM, a set of genotype specific nucleotide sequence "signatures" were selected from the 5' NCR, CORE, E1, and NS5B regions of the HCV genome. We evaluated the predictive power of these signatures for predicting the most common HCV genotypes and subtypes. We observed that nucleotide sequence signatures selected from NS5B and E1 regions generally demonstrated stronger discriminant power in differentiating major HCV genotypes and subtypes than that from 5' NCR and CORE regions. Two discriminant methods were used to build predictive models. Through 10 fold cross validation, over 99% prediction accuracy was achieved using both support vector machine (SVM) and random forest based classification methods in a dataset of 1134 sequences for NS5B and 947 sequences for E1. Prediction accuracy for each genotype is also reported.
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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.