2023
MonoNet: 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
2021
TITAN: T-cell receptor specificity prediction with bimodal attention networks
Weber A, Born J, Martínez M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics 2021, 37: i237-i244. PMID: 34252922, PMCID: PMC8275323, DOI: 10.1093/bioinformatics/btab294.Peer-Reviewed Original ResearchConceptsK-nearest neighborAttention networkLeverage transfer learningState-of-the-artK-nearest-neighbor (KNN) classifierInput data spaceK-NN classifierBimodal neural networkSMILES sequencesTransfer learningData augmentationAttention heatmapsCompetitive performanceNeural networkData spaceT cell receptorBoost performanceT-cell receptor sequencingClassifierNetworkImproved performanceT cellsPrediction of specificityPerformanceSequencing of T-cell receptor
2020
COSIFER: a Python package for the consensus inference of molecular interaction networks
Manica M, Bunne C, Mathis R, Cadow J, Ahsen M, Stolovitzky G, Martínez M. COSIFER: a Python package for the consensus inference of molecular interaction networks. Bioinformatics 2020, 37: 2070-2072. PMID: 33241320, PMCID: PMC8337002, DOI: 10.1093/bioinformatics/btaa942.Peer-Reviewed Original ResearchConceptsAdvent of high-throughput technologiesNetwork inferenceMolecular interaction networksHigh-throughput dataHigh-throughput technologiesState-of-the-artSupplementary dataExpression dataInteraction networkPython source codeInference servicesState-of-the-art methodologiesWeb servicesSource codeMolecular networksWeb-based platformRegulatory apparatusBioinformaticsPython packageConsensus strategyNetworkRobust networkInference methodsInferenceIndividual methods
2010
Quorum percolation in living neural networks
Cohen O, Keselman A, Moses E, Martínez M, Soriano J, Tlusty T. Quorum percolation in living neural networks. EPL (Europhysics Letters) 2010, 89: 18008. DOI: 10.1209/0295-5075/89/18008.Peer-Reviewed Original Research
2008
Development of input connections in neural cultures
Soriano J, Martínez M, Tlusty T, Moses E. Development of input connections in neural cultures. Proceedings Of The National Academy Of Sciences Of The United States Of America 2008, 105: 13758-13763. PMID: 18772389, PMCID: PMC2544527, DOI: 10.1073/pnas.0707492105.Peer-Reviewed Original ResearchConceptsRatio of excitatory to inhibitory neuronsConnectivity of networksGlobal informationNeuronal culturesInput connectionsSpontaneous bursting activityEmergence of connectionsGABA switchInhibitory neuronsInhibitory cellsMechanism of percolationNetworkCortical culturesNeuronal densityDay 7Hippocampal culturesCortical neuronsHippocampal neuronsInputNeuronsActive inputsBurst activityNeural culturesHippocampalConnection