María Rodríguez Martínez, PhD
Cards
About
Research
Publications
2024
Lessons learned from the IMMREP23 TCR-epitope prediction challenge
Nielsen M, Eugster A, Jensen M, Goel M, Tiffeau-Mayer A, Pelissier A, Valkiers S, Martínez M, Meynard-Piganeeau B, Greiff V, Mora T, Walczak A, Croce G, Moreno D, Gfeller D, Meysman P, Barton J. Lessons learned from the IMMREP23 TCR-epitope prediction challenge. ImmunoInformatics 2024, 16: 100045. DOI: 10.1016/j.immuno.2024.100045.Peer-Reviewed Original ResearchData leakageIssue of data leakageT cell receptorPerformance of proposed methodsPrediction challengeTraining dataBenchmarking CompetitionPMHC targetsRandom performanceTCR-pMHC interactionsInteraction of T-cell receptorsParticipating teamsCellular immune systemData setsTest dataImmune systemPerformanceNarrow spaceT-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 modelsCell-specific gene networks and drivers in rheumatoid arthritis synovial tissues
Pelissier A, Laragione T, Gulko P, Martínez M. Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues. Frontiers In Immunology 2024, 15: 1428773. PMID: 39161769, PMCID: PMC11330812, DOI: 10.3389/fimmu.2024.1428773.Peer-Reviewed Original ResearchTranscription factorsNatural killer TPhenotypic differencesGene regulatory networksCo-regulatory networkRNA-seq databaseCell typesFibroblast-like synoviocytesRNA-seqRegulatory networksGene networksTF clustersMultiple cell typesB cellsCell regulationKiller TRheumatoid arthritis synovial tissuePhenotypic groupsRA pathogenesisRA synovial tissuePathway changesTissue genesGenesCompare network propertiesComputational approachDo domain-specific protein language models outperform general models on immunology-related tasks?
Deutschmann N, Pelissier A, Weber A, Gao S, Bogojeska J, Martínez 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 tasksStochastic modeling of a gene regulatory network driving B cell development in germinal centers
Koshkin A, Herbach U, Martínez M, Gandrillon O, Crauste F. Stochastic modeling of a gene regulatory network driving B cell development in germinal centers. PLOS ONE 2024, 19: e0301022. PMID: 38547073, PMCID: PMC10977792, DOI: 10.1371/journal.pone.0301022.Peer-Reviewed Original ResearchConceptsGene regulatory network structureGene regulatory networksGene expression dataExpression dataB cell differentiationSingle-cellAssociated with cell developmentGC B cell differentiationStages of B-cell differentiationB cell developmentSelection of B cellsGene regulationRegulatory networksTranscriptome dataSystems biologyHigh-affinity antibodiesRegulatory mechanismsCell developmentGenesAdaptive immune systemMRNA distributionPlasmablast stageGerminal centersDifferentiationImmune system
2023
Proteomic-based stratification of intermediate-risk prostate cancer patients
Zhong Q, Sun R, Aref A, Noor Z, Anees A, Zhu Y, Lucas N, Poulos R, Lyu M, Zhu T, Chen G, Wang Y, Ding X, Rutishauser D, Rupp N, Rueschoff J, Poyet C, Hermanns T, Fankhauser C, Martínez M, Shao W, Buljan M, Neumann J, Beyer A, Hains P, Reddel R, Robinson P, Aebersold R, Guo T, Wild P. Proteomic-based stratification of intermediate-risk prostate cancer patients. Life Science Alliance 2023, 7: e202302146. PMID: 38052461, PMCID: PMC10698198, DOI: 10.26508/lsa.202302146.Peer-Reviewed Original ResearchConceptsGleason grade groupBiochemical recurrenceRisk of biochemical recurrenceIntermediate-risk patientsNon-aggressive diseaseProstate cancer managementProstate cancer patientsMultivariate Cox regressionHigh-risk groupPatient treatment decisionsProstatic adenocarcinomaGleason gradePrognostic indicatorMatched tumorCancer managementCox regressionCancer patientsGrade groupTreatment decisionsPatientsSurvival analysisRisk scoreClinical applicationGleasonProstateComputational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning
Martínez M, Barberis M, Niarakis A. Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning. ImmunoInformatics 2023, 12: 100029. DOI: 10.1016/j.immuno.2023.100029.Peer-Reviewed Original ResearchHigh-throughput experimental technologiesComputational biologyDevelopment of high-throughput experimental technologiesImmune systemHigh-throughput data analysisImmunological mechanismsMolecular functionsSystems biologyImmune-related diseasesOptimal immunotherapyTherapeutic optionsAutoimmune diseasesComplex disorderInterpretable machine learningMachine learning modelsCellular interactionsGeneration of computational modelsBiologyComputer scienceMachine learningMachine-learning modelsDiverse domainsLearning modelsExperimental technologyInterpretable machineConvergent evolution and B-cell recirculation in germinal centers in a human lymph node
Pelissier A, Stratigopoulou M, Donner N, Dimitriadis E, Bende R, Guikema J, Martinez M, van Noesel C. Convergent evolution and B-cell recirculation in germinal centers in a human lymph node. Life Science Alliance 2023, 6: e202301959. PMID: 37640448, PMCID: PMC10462906, DOI: 10.26508/lsa.202301959.Peer-Reviewed Original ResearchConceptsGerminal centersLymph nodesHuman lymph nodesGC responseHuman LNImmune responseDevelopment of autoimmune diseasesConvergent evolutionB cell clonesB cell recirculationEffective immune responseExpanded clonesLaser capture microdissectionPhylogenetic tree analysisIndividual germinal centersB cellsAutoimmune diseasesAntigen responseMouse modelModel antigenAntigenClonal diversityChemical representation learning for toxicity prediction
Born J, Markert G, Janakarajan N, Kimber T, Volkamer A, Martínez M, Manica M. Chemical representation learning for toxicity prediction. Digital Discovery 2023, 2: 674-691. DOI: 10.1039/d2dd00099g.Peer-Reviewed Original ResearchChemical language modelsLanguage modelMolecular property prediction tasksMolecular property prediction modelProperty prediction tasksMolecular property predictionExplicit supervisionAttention weightsMultiscale convolutionData augmentationPrediction taskToxicity datasetMolecular representationsProperty prediction modelsImproved accuracyModel reliabilityDatasetProperty predictionChemical representationsToxicity predictionPrediction uncertaintyUncertainty estimationDrug discoveryRepresentationPrediction modelExploring 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