T-cell receptor binding prediction: A machine learning revolution
Weber A, Pélissier A, MartÃnez M. T-cell receptor binding prediction: A machine learning revolution. ImmunoInformatics 2024, 15: 100040. DOI: 10.1016/j.immuno.2024.100040.Peer-Reviewed Original ResearchProtein language modelsT cell receptorExtract biological insightsUnlabeled protein sequencesProtein sequencesBinding specificityBiological insightsProtein modelsRepertoire dataDeep learning modelsSequenceBlack-box modelsUnsupervised clustering approachDataset biasEvolution of computational modelsLack of generalityLanguage modelImmunizing sequencesMachine learning effortsCompetitive performanceOpaque modelsBiological propertiesLearning modelsClustering approachSupervised modelsDo domain-specific protein language models outperform general models on immunology-related tasks?
Deutschmann N, Pelissier A, Weber A, Gao S, Bogojeska J, MartÃnez M. Do domain-specific protein language models outperform general models on immunology-related tasks? ImmunoInformatics 2024, 14: 100036. DOI: 10.1016/j.immuno.2024.100036.Peer-Reviewed Original ResearchProtein language modelsDevelopment of bioinformatics pipelinesAmino acid sequenceAntigen recognition capabilitiesBioinformatics pipelineAcid sequenceProtein functionDomain-specific modelsEvolutionary changesB cell receptorAdaptive immune system responsesImmune receptorsT cell receptorB cellsT cellsImmune system responseDownstream analytical tasksVector embeddingsRepresentation capabilityLanguage modelImmune responseEmbedding layerReceptorsAnalytical tasks
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