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
2020
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data
Oskooei A, Chau S, Weiss J, Sridhar A, Martínez M, Michel B. DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data. Studies In Computational Intelligence 2020, 914: 93-105. DOI: 10.1007/978-3-030-53352-6_9.Peer-Reviewed Original ResearchConvolutional autoencoderK-nearest neighbor classificationTraditional K-means clusteringFrequency domain featuresK-nearest neighborK-means clusteringShort-term memoryLSTM autoencoderUnsupervised methodDeep learningDomain featuresAutoencoderDBSCAN clusteringK-meansInterval time series dataUnsupervised identificationStress detectionData pointsTime series dataEngineering featuresNormal clusterLSTMSize of clusters