2024
Lessons learned from the IMMREP23 TCR-epitope prediction challenge
Nielsen 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
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