María Rodríguez Martínez, PhD, MSc
Associate Professor of Biomedical Informatics and Data ScienceCards
About
Research
Publications
2025
- Multi-modal clustering reveals event-free patient subgroup in colorectal cancer survivalJanakarajan N, Larghero G, Rodríguez Martínez M. Multi-modal clustering reveals event-free patient subgroup in colorectal cancer survival. Npj Systems Biology And Applications 2025, 11: 86. PMID: 40753169, PMCID: PMC12318085, DOI: 10.1038/s41540-025-00557-3.Peer-Reviewed Original Research
- Corrigendum to “T-cell receptor binding prediction: A machine learning revolution” [ImmunoInformatics, Volume 15, September 2024, 100040]Martínez M. Corrigendum to “T-cell receptor binding prediction: A machine learning revolution” [ImmunoInformatics, Volume 15, September 2024, 100040]. ImmunoInformatics 2025, 18: 100049. DOI: 10.1016/j.immuno.2025.100049.Peer-Reviewed Original Research
- Dissecting the role of CAR signaling architectures on T cell activation and persistence using pooled screens and single-cell sequencingCastellanos-Rueda R, Wang K, Forster J, Driessen A, Frank J, Martínez M, Reddy S. Dissecting the role of CAR signaling architectures on T cell activation and persistence using pooled screens and single-cell sequencing. Science Advances 2025, 11: eadp4008. PMID: 39951542, PMCID: PMC11827634, DOI: 10.1126/sciadv.adp4008.Peer-Reviewed Original ResearchConceptsChimeric antigen receptorT-cell phenotypeT cell responsesT cell activationCAR T cell phenotypesCAR T-cell biologyModulate T cell responsesT cell persistenceCAR-T therapySingle-cell sequencingT cell functionT cell biologyCorrelated in vitroT therapyT cellsAntigen receptorClinical outcomesCD40 costimulationCancer treatmentPhenotypeSignaling domainMembrane-proximal domainCostimulationCD40Screening approach
- Identification of clonally expanded T-cell receptor sequences in giant cell arteritisWeber A, Zulcinski M, Haroon-Rashid L, Kuszlewicz B, Driessen A, Newton D, Morgan A, Rodríguez Martínez M. Identification of clonally expanded T-cell receptor sequences in giant cell arteritis. Journal Of Autoimmunity 2025, 151: 103372. PMID: 39904264, DOI: 10.1016/j.jaut.2025.103372.Peer-Reviewed Original ResearchConceptsGiant cell arteritis patientsGiant cell arteritisAge-matched controlsT cellsTCR sequencesPeripheral blood TCR repertoireAssociated with giant cell arteritisGiant cell arteritis casesT cell clonal expansionT cell infiltrationT-cell receptor sequencingArterial wall inflammationTCR-VTCR repertoirePeripheral bloodTRBV4Clonal expansionPaired bloodGranuloma formationTCRB repertoireTarget antigenWall inflammationHuman antigensPatientsTCR
2024
- Phenotype driven data augmentation methods for transcriptomic dataJanakarajan N, Graziani M, Martínez M. Phenotype driven data augmentation methods for transcriptomic data. Bioinformatics Advances 2024, 5: vbaf124. PMID: 40487930, PMCID: PMC12141816, DOI: 10.1093/bioadv/vbaf124.Peer-Reviewed Original ResearchAugmentation methodSupervised learning tasksData augmentation methodData augmentation strategyData augmentation methodsApplication of machine learning methodsSynthetic data pointsMachine learning methodsMachine learning modelsClass imbalanceLearning methodsLearning modelsTranscriptome dataLearning tasksAugmentation strategiesGene expression dataParametric estimationDataData pointsGamma-PoissonExpression dataNon-parametric
- Lessons learned from the IMMREP23 TCR-epitope prediction challengeNielsen 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 space
- Immune digital twins for complex human pathologies: applications, limitations, and challenges.Niarakis A, Laubenbacher R, An G, Ilan Y, Fisher J, Flobak Å, Reiche K, Rodríguez Martínez M, Geris L, Ladeira L, Veschini L, Blinov ML, Messina F, Fonseca LL, Ferreira S, Montagud A, Noël V, Marku M, Tsirvouli E, Torres MM, Harris LA, Sego TJ, Cockrell C, Shick AE, Balci H, Salazar A, Rian K, Hemedan AA, Esteban-Medina M, Staumont B, Hernandez-Vargas E, Martis B S, Madrid-Valiente A, Karampelesis P, Sordo Vieira L, Harlapur P, Kulesza A, Nikaein N, Garira W, Malik Sheriff RS, Thakar J, Tran VDT, Carbonell-Caballero J, Safaei S, Valencia A, Zinovyev A, Glazier JA. Immune digital twins for complex human pathologies: applications, limitations, and challenges. NPJ Syst Biol Appl 2024, 10: 141. PMID: 39616158, DOI: 10.1038/s41540-024-00450-5.Peer-Reviewed Original Research
- BACH1 as a key driver in rheumatoid arthritis fibroblast-like synoviocytes identified through gene network analysisPelissier A, Laragione T, Harris C, Rodríguez Martínez M, Gulko P. BACH1 as a key driver in rheumatoid arthritis fibroblast-like synoviocytes identified through gene network analysis. Life Science Alliance 2024, 8: e202402808. PMID: 39467637, PMCID: PMC11519322, DOI: 10.26508/lsa.202402808.Peer-Reviewed Original ResearchConceptsTranscription factorsRegulatory networksRA fibroblast-like synoviocytesFormation of lamellipodiaGene regulatory networksCentral regulatorGene network analysisPolarity formationContribution to diseaseGene expression studiesKnockdown of BACH1Fibroblast-like synoviocytesRheumatoid arthritis fibroblast-like synoviocytesFatty acid metabolismGene expression signaturesActin fibersCo-regulationRNA sequencingExpression studiesGenesCell adhesionAcid metabolismBach1Expression signaturesSignificant TFs
- T-cell receptor binding prediction: A machine learning revolutionWeber 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
- Identification of single-cell blasts in pediatric acute myeloid leukemia using an autoencoderDriessen A, Unger S, Nguyen A, Ries R, Meshinchi S, Kreutmair S, Alberti C, Sumazin P, Aplenc R, Redell M, Becher B, Rodríguez Martínez M. Identification of single-cell blasts in pediatric acute myeloid leukemia using an autoencoder. Life Science Alliance 2024, 7: e202402674. PMID: 39191488, PMCID: PMC11358707, DOI: 10.26508/lsa.202402674.Peer-Reviewed Original ResearchConceptsAcute myeloid leukemiaPediatric acute myeloid leukemiaBlast immunophenotypeImmunotherapy targetMyeloid leukemiaImmunophenotypic compositionAggressive blood cancerPediatric AML patientsIdentification of immunotherapy targetsFlow cytometry profilesKMT2A rearrangementAML patientsAML treatmentMalignant blastsRelapse rateAML blastsMalignant cellsPoor prognosisDisease progressionRelapseImmunophenotypeBlood cancerPatientsLeukemiaMolecular features
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101 College Street, PO Box 208009
New Haven, CT 06520-8009
United States
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- Department of Biomedical Informatics & Data Science- Academic Office - 101 College Street, Fl 10, Rm 1021R - New Haven, CT 06510