Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks.

Bioinformatics (Oxford, England) - 1 May 2020

Yin Rui, Luusua Emil, Dabrowski Jan, Zhang Yu, Kwoh Chee Keong

Abstract excerpt

MOTIVATION: Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. The goal of this work is to predict whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data....

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Attention
Share to Influenza A virus
Share to Influenza, Human

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Neural Networks, Computer

Source

Identifiers

PMID: 31999330
DOI: 10.1093/bioinformatics/btaa050

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks.PMID 31999330Read ↗

PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information.Semantic proximity Read ↗

Predicting the Trend of SARS-CoV-2 Mutation Frequencies Using Historical DataSemantic proximity Read ↗

TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution.Semantic proximity Read ↗

Intelligent prediction and biological validation of the high reassortment potential of avian H5N1 and human H3N2 influenza virusesSemantic proximity Read ↗

Predicting genetic evolution of viruses to identify suitable vaccines using artificial intelligence.Semantic proximity Read ↗

Predicting the Mutating Distribution at Antigenic Sites of the Influenza Virus.Semantic proximity Read ↗

MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populationsSemantic proximity Read ↗

Predictive evolutionary modelling for influenza virus by site-based dynamics of mutationsSemantic proximity Read ↗

MetaFluAD: meta-learning for predicting antigenic distances among influenza virusesSemantic proximity Read ↗

Characterization of the evolutionary dynamics of influenza A H3N2 hemagglutininSemantic proximity Read ↗

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.Semantic proximity Read ↗

Predicting the trend of SARS-CoV-2 mutation frequencies using historical data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.