ArticleScientific reports2019
Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Impact of Point-of-Care Testing on Diagnosis, Treatment, and Surveillance of Vaccine-Preventable Viral Infections.Diagnostics (Basel, Switzerland) · 2025Review
- Automated classification of giant virus genomes using a random forest model built on trademark protein families.Npj viruses · 2024Article
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- Comparison of patients with hepatitis B virus-associated hepatocellular carcinoma: Data from two hospitals from Turkey and China.Portal hypertension & cirrhosis · 2023Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature.Scientific reports · 2022Article
- Power of big data to improve patient care in gastroenterology.Frontline gastroenterology · 2022Article
- Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions.Frontiers in artificial intelligence · 2022Review
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Review
- Article
- Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection.Microorganisms · 2020Review
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Variable viral mutant signatures develop within individual patients due to the low-fidelity replication of the viral polymerase creating 'quasispecies' populations. Here we present the first comprehensive survey of the diversity of HBV quasispecies through ultra-deep sequencing of the complete HBV genome across two distinct European and Asian patient populations. Seroconversion to the HBV e antigen (HBeAg) represents a critical clinical waymark in infected individuals. Using a machine learning approach, a model was developed to determine the viral variants that accurately classify HBeAg status. Serial surveys of patient quasispecies populations and advanced analytics will facilitate clinical decision support for chronic HBV infection and direct therapeutic strategies through improved patient stratification.
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