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 models
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 availabilityCodeUsersDecisionPerformance
2021
On the feasibility of deep learning applications using raw mass spectrometry data
Cadow J, Manica M, Mathis R, Reddel R, Robinson P, Wild P, Hains P, Lucas N, Zhong Q, Guo T, Aebersold R, Martínez M. On the feasibility of deep learning applications using raw mass spectrometry data. Bioinformatics 2021, 37: i245-i253. PMID: 34252933, PMCID: PMC8275322, DOI: 10.1093/bioinformatics/btab311.Peer-Reviewed Original ResearchConceptsRaw mass spectrometry dataDeep learning modelsRaw MS dataMass spectrometry dataClassification performanceDeep learningMS dataMass spectrometryLearning modelsSpectrometry dataApplication of deep learningMS imagesNatural image classificationDeep learning applicationsPrivacy of individualsTransfer learning techniqueData-independent-acquisitionMS2 spectraClassification taskData processing pipelinesClassification labelsImage classificationFeature vectorTransfer learningSample sparsity