ArticleBMC ecology and evolution2021
DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites.
Article in BMC ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- VISDB 2.0: A manually curated resource of viral integration sites and their regulatory maps in human diseases.Scientific data · 2026Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Review
- Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.International journal of molecular sciences · 2024Article
- VIS Atlas: A Database of Virus Integration Sites in Human Genome from NGS Data to Explore Integration Patterns.Genomics, proteomics & bioinformatics · 2023Article
- Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome.Patterns (New York, N.Y.) · 2023Article
- Hepatitis B and circadian rhythm of the liver.World journal of gastroenterology · 2022Review
- Decision Support System for Predicting Survivability of Hepatitis Patients.Frontiers in public health · 2022Article
- Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions.Frontiers in artificial intelligence · 2022Review
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation.
resultsAn attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV.
conclusionsDeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis.
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