2023
FLAN: 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 availabilityCodeUsersDecisionPerformanceMonoNet: 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
PaccMann: a web service for interpretable anticancer compound sensitivity prediction
Cadow J, Born J, Manica M, Oskooei A, Martínez M. PaccMann: a web service for interpretable anticancer compound sensitivity prediction. Nucleic Acids Research 2020, 48: w502-w508. PMID: 32402082, PMCID: PMC7319576, DOI: 10.1093/nar/gkaa327.Peer-Reviewed Original ResearchConceptsWeb servicesAttention-based neural networkState-of-the-art methodsState-of-the-artOutputs confidence scoresCompound structure informationInteractive editorModel decision makingChemical sub-structuresAttention heatmapsNeural networkWeb-based platformModel interpretationEfficient validationConfidence scoresDrug repositioningCompound structureDrug compoundsWebSource of informationStructural informationCompoundsBiomolecular samplesDecision makingPotential compounds
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