Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning
Martínez M, Barberis M, Niarakis A. Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning. ImmunoInformatics 2023, 12: 100029. DOI: 10.1016/j.immuno.2023.100029.Peer-Reviewed Original ResearchHigh-throughput experimental technologiesComputational biologyDevelopment of high-throughput experimental technologiesImmune systemHigh-throughput data analysisImmunological mechanismsMolecular functionsSystems biologyImmune-related diseasesOptimal immunotherapyTherapeutic optionsAutoimmune diseasesComplex disorderInterpretable machine learningMachine learning modelsCellular interactionsGeneration of computational modelsBiologyComputer scienceMachine learningMachine-learning modelsDiverse domainsLearning modelsExperimental technologyInterpretable machineMonoNet: 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