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