{ "data_id": "87", "name": "micro-mass", "exact_name": "micro-mass", "version": 2, "version_label": "2", "description": "**Author**: Pierre Mah\u00e9, Jean-Baptiste Veyrieras \n**Source**: [original](http:\/\/www.openml.org\/d\/1514) - UCI \n**Please cite**: \n\n* Dataset Title: MicroMass - Pure (pure spectra version) \n\n* Abstract: \nA dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data. \n\n* Source:\n\nPierre Mah\u00e9, pierre.mahe '@' biomerieux.com, bioM\u00e9rieux\nJean-Baptiste Veyrieras, jean-baptiste.veyrieras '@' biomerieux.com, bioM\u00e9rieux\n\n* Data Set Information:\n\nThis MALDI-TOF dataset consists in:\nA) A reference panel of 20 Gram positive and negative bacterial species covering 9 genera among which several species are known to be hard to discriminate by mass spectrometry (MALDI-TOF). Each species was represented by 11 to 60 mass spectra obtained from 7 to 20 bacterial strains, constituting altogether a dataset of 571 spectra obtained from 213 strains. The spectra were obtained according to the standard culture-based workflow used in clinical routine in which the microorganism was first grown on an agar plate for 24 to 48 hours, before a portion of colony was picked, spotted on a MALDI slide and a mass spectrum was acquired. \nB) Based on this reference panel, a dedicated in vitro mock-up mixture dataset was constituted. For that purpose we considered 10 pairs of species of various taxonomic proximity:\n* 4 mixtures, labelled A, B, C and D, involved species that belong to the same genus, \n* 2 mixtures, labelled E and F, involved species that belong to distinct genera, but to the same Gram type, \n* 4 mixtures, labelled G, H, I and J, involved species that belong to distinct Gram types. \nEach mixture was represented by 2 pairs of strains, which were mixed according to the following 9 concentration ratios : 1:0, 10:1, 5:1, 2:1, 1:1, 1:2, 1:5, 1:10, 0:1. Two replicate spectra were acquired for each concentration ratio and each couple of strains, leading altogether to a dataset of 360 spectra, among which 80 are actually pure sample spectra.\n\n* Relevant Papers:\n\nMah\u00e9 et al. (2014). Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum. Bioinformatics.\n\nVervier et al., A benchmark of support vector machines strategies for microbial identification by mass-spectrometry data, submitted\n\n\n\n\n\n", "format": "ARFF", "uploader": "Rafael Gomes Mantovani", "uploader_id": 64, "visibility": "public", "creator": "\"Pierre Mah\u00e9\",\"Jean-Baptiste Veyrieras\"", "contributor": null, "date": "2015-06-01 16:57:25", "update_comment": null, "last_update": "2015-06-01 16:57:25", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/1593707\/phpHyLSNF", "default_target_attribute": "Class", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "micro-mass", "* Dataset Title: MicroMass - Pure (pure spectra version) * Abstract: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data. * Source: Pierre Mah\u00e9, pierre.mahe '@' biomerieux.com, bioM\u00e9rieux Jean-Baptiste Veyrieras, jean-baptiste.veyrieras '@' biomerieux.com, bioM\u00e9rieux * Data Set Information: This MALDI-TOF dataset consists in: A) A reference panel of 20 Gram positive and negative bacterial species covering 9 genera among which s " ], "weight": 5 }, "qualities": { "NumberOfInstances": 571, "NumberOfFeatures": 1301, "NumberOfClasses": 20, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 1300, "NumberOfSymbolicFeatures": 1, "AutoCorrelation": 0.6982456140350877, "CfsSubsetEval_DecisionStumpAUC": 0.8836144467045589, "CfsSubsetEval_DecisionStumpErrRate": 0.28021015761821366, "CfsSubsetEval_DecisionStumpKappa": 0.7014232723826331, "CfsSubsetEval_NaiveBayesAUC": 0.8836144467045589, "CfsSubsetEval_NaiveBayesErrRate": 0.28021015761821366, "CfsSubsetEval_NaiveBayesKappa": 0.7014232723826331, "CfsSubsetEval_kNN1NAUC": 0.8836144467045589, "CfsSubsetEval_kNN1NErrRate": 0.28021015761821366, "CfsSubsetEval_kNN1NKappa": 0.7014232723826331, 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