ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2021
DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 20 citations in OpenAlex.
- VISDB 2.0: A manually curated resource of viral integration sites and their regulatory maps in human diseases.Scientific data · 2026Article
- Article
- Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome.Patterns (New York, N.Y.) · 2023Article
- NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.Genomics, proteomics & bioinformatics · 2022Article
- Designing optimal convolutional neural network architecture using differential evolution algorithm.Patterns (New York, N.Y.) · 2022Article
- DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genomic instability and carcinogenesis. Here, a new deep convolutional neural network (CNN) model is developed with attention architecture, namely DeepVISP, for accurately predicting oncogenic virus integration sites (VISs) in the human genome. Using the curated benchmark integration data of three viruses, hepatitis B virus (HBV), human herpesvirus (HPV), and Epstein-Barr virus (EBV), DeepVISP achieves high accuracy and robust performance for all three viruses through automatically learning informative features and essential genomic positions only from the DNA sequences. In comparison, DeepVISP outperforms conventional machine learning methods by 8.43-34.33% measured by area under curve (AUC) value enhancement in three viruses. Moreover, DeepVISP can decode
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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.