Interpretable detection of novel human viruses from genome sequencing data
Explore this paper's citation graph
Summary
It is shown 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.
- Type
- preprint
- Published
- 2020-01-30
- Cited by
- 60
- References
- 92
- Access
- Open access
- OpenAlex
- https://openalex.org/W3003934875
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:213848410
Keywords
Interpretability, Computer science, Deep learning, Convolutional neural network, Artificial intelligence
References
- Predicting host tropism of influenza A virus proteins using random forest
- Mason – A Read Simulator for Second Generation Sequencing Data
- CDD/SPARCLE: functional classification of proteins via subfamily domain architectures
- Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
- Assessing the Evidence Supporting Fruit Bats as the Primary Reservoirs for Ebola Viruses
- Sequence-Based Classification of Select Agents: A Brighter Line
- Structural Insights into SraP-Mediated Staphylococcus aureus Adhesion to Host Cells
- Incentive Compatible Privacy-Preserving Distributed Classification
- Optimal ensemble averaging of neural networks
- Sequence logos: a new way to display consensus sequences.
- Basic local alignment search tool.
- Virus taxonomy : eighth report of the International Committee on Taxonomy of Viruses
- Experimental adaptation of an influenza H5 haemagglutinin (HA) confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets
- Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration
- The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening
- Emerging bacterial pathogens: the past and beyond
- BLAST+: architecture and applications
- Clock Rooting Further Demonstrates that Guinea 2014 EBOV is a Member of the Zaïre Lineage
- Airborne Transmission of Influenza A/H5N1 Virus Between Ferrets
Cited by
- Mapping the Landscape of Artificial Intelligence Applications against COVID-19
- Pathogenic virus detection method based on multi-model fusion
- Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic
- Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning
- Identifying and prioritizing potential human-infecting viruses from their genome sequences
- Estimating the Distribution of Viral Taxa in Next-generation Sequencing data using Artificial Neural Networks
- Role of Emerging Technologies in COVID 19: Analyses, Predictions, and Future Countermeasures
- A deep learning framework for real-time detection of novel pathogens during sequencing
- Characterizing and Evaluating the Zoonotic Potential of Novel Viruses Discovered in Vampire Bats
- Explainable deep neural networks for novel viral genome prediction
- A Novel Training Strategy for Deep Learning Model Compression Applied to Viral Classifications
- Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples
- Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus
- Correcting the Estimation of Viral Taxa Distributions in Next-Generation Sequencing Data after Applying Artificial Neural Networks
- Chaos game representation and its applications in bioinformatics
- Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection
- The science of the host–virus network
- Artificial Intelligence applications addressing different aspects of the Covid-19 crisis and key technological solutions for future epidemics control
- Role of Emerging Technologies in COVID 19: Analyses, Predictions, and Future Countermeasures (Preprint)
- AMAISE: a machine learning approach to index-free sequence enrichment
Related papers
- ML Interpretability: Simple Isn't Easy
- When consumers need more interpretability of artificial intelligence (AI) recommendations? The effect of decision-making domains
- Effects of mixed sample data augmentation on interpretability of neural networks
- Assessing the Interpretability of Programmatic Policies with Large Language Models