2024
Cell-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 approachStochastic 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
2022
Attention-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 values
2019
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
Oskooei A, Manica M, Mathis R, Martínez M. Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer. Scientific Reports 2019, 9: 15918. PMID: 31685861, PMCID: PMC6828742, DOI: 10.1038/s41598-019-52093-w.Peer-Reviewed Original ResearchConceptsMembrane receptor pathwayDrug sensitivity predictionProtein-protein interaction networkDrug sensitivityGenomics of Drug SensitivityDrug targetsGene expression dataIGFR signaling pathwaysAssignment of high weightsBiomarker identificationExpression dataInteraction networkSensitivity predictionSignaling pathwaySignaling pathway inhibitorsReceptor pathwayTree ensemblesPathway inhibitorPathwayGenomeGenesGDSCNeighborhoods of influenceIdentificationSynthetic datasetsToward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Manica M, Oskooei A, Born J, Subramanian V, Sáez-Rodríguez J, Martínez M. Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics 2019, 16: 4797-4806. PMID: 31618586, DOI: 10.1021/acs.molpharmaceut.9b00520.Peer-Reviewed Original ResearchConceptsConvolutional encoderReceptor tyrosine kinasesProtein-protein interaction networkAttention-based encoderStructural similarity indexSelection of encodingDrug designDrug sensitivity predictionGene expression profilesIn silico predictionSensitivity predictionAttention weightsLeukemia cell linesSMILES sequencesInformative genesGene expression profiles of tumorsApoptotic processInteraction networkExpression profiles of tumorsBaseline modelIntracellular interactionsEncodingTyrosine kinaseDevelopment of personalized therapiesGenes
2018
The number of titrated microRNA species dictates ceRNA regulation
Chiu H, Martínez M, Komissarova E, Llobet-Navas D, Bansal M, Paull E, Silva J, Yang X, Sumazin P, Califano A. The number of titrated microRNA species dictates ceRNA regulation. Nucleic Acids Research 2018, 46: gky286-. PMID: 29684207, PMCID: PMC5961349, DOI: 10.1093/nar/gky286.Peer-Reviewed Original ResearchConceptsCo-regulationPrediction of gene expressionMiRNA speciesTumor suppressor PTENCeRNA regulationGene expressionCeRNA interactionsMicroRNA speciesBiochemical assaysMultiple miRNAsSpeciesOncogene CCND1Non-tumour contextsRegulating microRNAsMiRNAsInteraction kineticsEndogenous RNAIndependent tumorsCeRNA networkHIF1AInteractomeCeRNAGenesRNAOncogene
2017
High-throughput validation of ceRNA regulatory networks
Chiu H, Martínez M, Bansal M, Subramanian A, Golub T, Yang X, Sumazin P, Califano A. High-throughput validation of ceRNA regulatory networks. BMC Genomics 2017, 18: 418. PMID: 28558729, PMCID: PMC5450082, DOI: 10.1186/s12864-017-3790-7.Peer-Reviewed Original ResearchConceptsCeRNA interactionsMiRNA targetsCancer genesCompetitive endogenous RNAHigh-throughput validationIndirect regulatory influencesCellular contextMiRNA activityTumor contextMCF7 cellsTarget abundanceGenesPhysiological relevanceExperimental assaysCompetitive endogenous RNA networkAbundanceConclusionsOur resultsPredicted targetsTCGA profileMiRNAsRegulatory influenceEndogenous RNAResultsToRNABackgroundMicroRNAs
2012
Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis
Martínez M, Corradin A, Klein U, Álvarez M, Toffolo G, di Camillo B, Califano A, Stolovitzky G. Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis. Proceedings Of The National Academy Of Sciences Of The United States Of America 2012, 109: 2672-2677. PMID: 22308355, PMCID: PMC3289327, DOI: 10.1073/pnas.1113019109.Peer-Reviewed Original ResearchConceptsB-cell exitTranscriptional regulatory modulesTerminal differentiationTerminal differentiation of B cellsSelf-regulatory interactionsGene expression profiling dataMechanisms of lymphomagenesisExpression profiling dataMature human B cellsRegulatory modulesGene regulationT cell signalingB cellsCellular statesDifferentiation of B cellsHuman B cellsGerminal centersTumorigenic alterationsGenesQuantitative kinetic modelMemory B cellsAssociated with lymphomagenesisFeedback loopLymphomagenesisT cells
2008
Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation
Shalem O, Dahan O, Levo M, Martinez M, Furman I, Segal E, Pilpel Y. Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation. Molecular Systems Biology 2008, 4: msb200859. PMID: 18854817, PMCID: PMC2583085, DOI: 10.1038/msb.2008.59.Peer-Reviewed Original ResearchConceptsInduced genesRepressed genesMRNA productionTranscriptional responses to stressGenome-wide interplayResponse to stressResponse to environmental changesMRNA decay ratesCondition-specific changesSteady-state levelsTranscriptional arrestGenesTranscriptomeEnvironmental changesMRNARegulatory armYeastMicroarrayDegradationProduction