ArticleNAR genomics and bioinformatics2021
Interpretable detection of novel human viruses from genome sequencing data.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Cyber-biological convergence: a systematic review and future outlook.Frontiers in bioengineering and biotechnology · 2024Pooled it
- BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.Bioinformatics (Oxford, England) · 2026Article
- Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.Scientific reports · 2026Article
- An improved dataset for predicting mammal infecting viruses from genetic sequence information.PLoS computational biology · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Mining Porcine Blood Whole-DNA Sequencing Datasets to Uncover Pig Viromes: An Exploratory Application to Identify Potential Infecting Agents of an Undefined Disease Outbreak.Veterinary sciences · 2025Article
- Hidden challenges in evaluating spillover risk of zoonotic viruses using machine learning models.Communications medicine · 2025Article
- NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search.Computational and structural biotechnology journal · 2024Article
- DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification.BMC bioinformatics · 2024Article
- Article
- RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes.GigaScience · 2024Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Characterisation of putative novel tick viruses and zoonotic risk prediction.Ecology and evolution · 2024Article
- The International Virus Bioinformatics Meeting 2023.Viruses · 2023Article
- DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.Briefings in bioinformatics · 2023Article
- Using machine learning to detect coronaviruses potentially infectious to humans.Scientific reports · 2023Article
- A deep learning approach reveals unexplored landscape of viral expression in cancer.Nature communications · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- SARS-CoV-2 virus classification based on stacked sparse autoencoder.Computational and structural biotechnology journal · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.
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.