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 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. 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 hypermutationFLAN: feature-wise latent additive neural models for biological applications
Nguyen A, Vasilaki S, Martínez M. FLAN: feature-wise latent additive neural models for biological applications. Briefings In Bioinformatics 2023, 24: bbad056. PMID: 37031956, PMCID: PMC10199769, DOI: 10.1093/bib/bbad056.Peer-Reviewed Original ResearchConceptsLearning modelsDeep neural networksDeep learning modelsMachine learning modelsBenchmark datasetsLatent spaceNeural modelNeural networkAlgorithmic decisionsEnd-usersBlack-boxComplex datasetsAggregated representationDecision processCritical scenariosImpressive resultsDatasetIndividual featuresRepresentationBiological domainData availabilityCodeUsersDecisionPerformanceBenchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report
Meysman P, Barton J, Bravi B, Cohen-Lavi L, Karnaukhov V, Lilleskov E, Montemurro A, Nielsen M, Mora T, Pereira P, Postovskaya A, Martínez M, Fernandez-de-Cossio-Diaz J, Vujkovic A, Walczak A, Weber A, Yin R, Eugster A, Sharma V. Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report. ImmunoInformatics 2023, 9: 100024. DOI: 10.1016/j.immuno.2023.100024.Peer-Reviewed Original ResearchIs Attention Interpretation? A Quantitative Assessment on Sets
Haab J, Deutschmann N, Martínez M. Is Attention Interpretation? A Quantitative Assessment on Sets. Communications In Computer And Information Science 2023, 1752: 303-321. DOI: 10.1007/978-3-031-23618-1_21.Peer-Reviewed Original ResearchBinary classification problemInterpretation of attentionClassification problemAttention mechanismSynthetic datasetsUnordered collectionClassification performanceSilent failuresMachine learningGlobal labelsData modalitiesIndividual instancesAttention distributionAttention scoresAttention patternsData pointsSub-componentsInstancesDatasetEditorial: Systems biology, women in science 2021/22: Data and model integration
Martínez M, Lao A, Torres L. Editorial: Systems biology, women in science 2021/22: Data and model integration. Frontiers In Systems Biology 2023, 3: 1134055. DOI: 10.3389/fsysb.2023.1134055.Peer-Reviewed Original ResearchMonoNet: enhancing interpretability in neural networks via monotonic features
Nguyen A, Moreno D, Le-Bel N, Martínez M. MonoNet: enhancing interpretability in neural networks via monotonic features. Bioinformatics Advances 2023, 3: vbad016. PMID: 37143924, PMCID: PMC10152389, DOI: 10.1093/bioadv/vbad016.Peer-Reviewed Original ResearchNeural networkMonotonicity constraintsHigh-stakes scenariosInformation-theoretic analysisMachine learning modelsMedical informaticsNeural modelLearning capabilityLearning modelsBioinformatics Advances</i>Monotonous featuresComputational biologyEnhance interpretationModeling capabilitiesDatasetInterpretable modelsLearning processSample dataNetworkPower modelLearningSupplementary dataConstraintsPerformanceInformatics
2022
Single-Cell Map of Childhood Acute Myeloid Leukaemia Using Variational Auto-Encoders
Driessen A, Unger S, Nguyen A, Kreutmair S, Alberti C, Sethi A, Aplenc R, Redell M, Martinez M, Becher B. Single-Cell Map of Childhood Acute Myeloid Leukaemia Using Variational Auto-Encoders. Blood 2022, 140: 2265-2266. DOI: 10.1182/blood-2022-169468.Peer-Reviewed Original ResearchAttention-Based Interpretable Regression of Gene Expression in Histology
Graziani M, Marini N, Deutschmann N, Janakarajan N, Müller H, Martínez M. Attention-Based Interpretable Regression of Gene Expression in Histology. Lecture Notes In Computer Science 2022, 13611: 44-60. DOI: 10.1007/978-3-031-17976-1_5.Peer-Reviewed Original ResearchCancer molecular subtypesGene expressionPredicting RNA structureMolecular subtypesTreatment responsePatient stratificationColorectal cancerGene expression profilesCancer tissuesRNA structurePathology UnitMicroscopic appearancePatient recommendationsHistologyCancerGenesExpression profilesExpression valuesDECODE: a computational pipeline to discover T cell receptor binding rules
Papadopoulou I, Nguyen A, Weber A, Martínez M. DECODE: a computational pipeline to discover T cell receptor binding rules. Bioinformatics 2022, 38: i246-i254. PMID: 35758821, PMCID: PMC9235487, DOI: 10.1093/bioinformatics/btac257.Peer-Reviewed Original ResearchConceptsT cell receptor bindingT cell receptorComputational pipelineTCR-epitope bindingBlack-box natureSequence motifsSequencing technologiesSupplementary dataBlack-box modelsBiochemical rulesMachine learningVisualization toolsComputational rulesDecodingData abundanceSequenceBioinformaticsEasy-to-useAdaptive immune responsesBindingBinding propertiesT cell-based therapiesT-cell receptor sequencingTCR bindingTCR specificityPCfun: a hybrid computational framework for systematic characterization of protein complex function
Sharma V, Fossati A, Ciuffa R, Buljan M, Williams E, Chen Z, Shao W, Pedrioli P, Purcell A, Martínez M, Song J, Manica M, Aebersold R, Li C. PCfun: a hybrid computational framework for systematic characterization of protein complex function. Briefings In Bioinformatics 2022, 23: bbac239. PMID: 35724564, PMCID: PMC9310514, DOI: 10.1093/bib/bbac239.Peer-Reviewed Original ResearchConceptsProtein complex functionGO termsProtein complex databasesGene Ontology (GONatural language processing techniquesRandom forestLanguage processing techniquesNearest neighborsProtein complexesSystematic annotationCellular statesBiological functionsBiological processesQuery vectorComplex queriesWord vectorsUnsupervised approachSupervised approachChild termsProteinMolecular biologySubunit compositionComplex databasesComplex functionsComputational frameworkMulti-Scale Modeling Recapitulates the Effect of Genetic Alterations Associated With Diffuse Large B-Cell Lymphoma in the Germinal Center Dynamics
Tejero E, Mao Q, Lashgari D, García-Valiente R, Robert P, Meyer-Hermann M, Martínez M, Guikema J, Hoefsloot H, van Kampen A. Multi-Scale Modeling Recapitulates the Effect of Genetic Alterations Associated With Diffuse Large B-Cell Lymphoma in the Germinal Center Dynamics. Frontiers In Systems Biology 2022, 2: 864690. DOI: 10.3389/fsysb.2022.864690.Peer-Reviewed Original ResearchDiffuse large B-cell lymphomaLarge B-cell lymphomaB-cell lymphoma 6B-cell lymphomaB lymphocyte-induced maturation protein-1B cell lymphoma 6 expressionInterferon regulatory factor 4B cellsGC reactionPC differentiationTranscription factor dynamicsPlasma cellsSubtype of non-Hodgkin lymphomaAccumulation of B cellsGC B cell phenotypeFactor kappa-light-chain-enhancer of activated B cellsNon-Hodgkin's lymphomaNuclear factor kappa-light-chain-enhancer of activated B cellsNuclear factor kappa-light-chain-enhancerB-cell phenotypeGC B cell populationB cell populationsGC B cellsB cell differentiationRegulatory factor 4
2021
The Multiple Dimensions of Networks in Cancer: A Perspective
Axenie C, Bauer R, Martínez M. The Multiple Dimensions of Networks in Cancer: A Perspective. Symmetry 2021, 13: 1559. DOI: 10.3390/sym13091559.Peer-Reviewed Original Research