2024
T-cell receptor binding prediction: A machine learning revolution
Weber A, Pélissier A, Martínez 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 models
2023
Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires
Pelissier A, Luo S, Stratigopoulou M, Guikema J, Martínez M. Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires. Frontiers In Immunology 2023, 14: 1123968. PMID: 37138881, PMCID: PMC10150052, DOI: 10.3389/fimmu.2023.1123968.Peer-Reviewed Original ResearchConceptsClonal diversityB cell receptorB cellsB cell diversityHigh-throughput sequencing technologyAlignment-free methodsAnalysis of immune repertoiresAlignment-based methodsPatterns of variationB cell receptor sequencesSequencing technologiesClonal clustersClonal identificationB cell repertoireActivated B cellsAdaptive immune responsesDiversity indexHigh-throughput characterizationAdaptive immune systemShort sequencesClonal characterizationClonal familiesClonesRepertoire dataSomatic hypermutation