Data
micro-mass

micro-mass

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
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Author: Pierre Mahé, Jean-Baptiste Veyrieras Source: [UCI](https://archive.ics.uci.edu/ml/datasets/MicroMass) - 2014 Please cite: ### Description MicroMass (pure spectra version) is a dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data. ### Source ``` Pierre Mahé, pierre.mahe '@' biomerieux.com, bioMérieux Jean-Baptiste Veyrieras, jean-baptiste.veyrieras '@' biomerieux.com, bioMérieux ``` ### 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 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 the colony was picked, spotted on a MALDI slide and a mass spectrum was acquired. b) 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: * 4 mixtures, labeled A, B, C and D, involved species that belong to the same genus, * 2 mixtures, labeled E and F, involved species that belong to distinct genera, but to the same Gram type, * 4 mixtures, labeled G, H, I and J, involved species that belong to distinct Gram types. Each 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. ### Relevant Papers Mahé et al. (2014). Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum. Bioinformatics. Vervier et al., A benchmark of support vector machines strategies for microbial identification by mass-spectrometry data, submitted

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V950numeric30 unique values
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0 missing
V951numeric38 unique values
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V440numeric82 unique values
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V696numeric1 unique values
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V957numeric73 unique values
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0 missing
V192numeric87 unique values
0 missing
V448numeric1 unique values
0 missing
V704numeric81 unique values
0 missing
V960numeric92 unique values
0 missing
V1216numeric3 unique values
0 missing
V193numeric1 unique values
0 missing
V449numeric82 unique values
0 missing
V705numeric70 unique values
0 missing
V961numeric20 unique values
0 missing
V1217numeric15 unique values
0 missing
V194numeric27 unique values
0 missing
V450numeric19 unique values
0 missing
V706numeric44 unique values
0 missing
V962numeric1 unique values
0 missing
V1218numeric1 unique values
0 missing
V195numeric21 unique values
0 missing
V451numeric32 unique values
0 missing
V707numeric23 unique values
0 missing
V963numeric7 unique values
0 missing
V1219numeric64 unique values
0 missing
V196numeric2 unique values
0 missing
V452numeric46 unique values
0 missing
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0 missing
V964numeric109 unique values
0 missing

62 properties

571
Number of instances (rows) of the dataset.
1301
Number of attributes (columns) of the dataset.
20
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
1300
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
94709.23
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
8.92
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
571
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
306.34
Third quartile of kurtosis among attributes of the numeric type.
2882892.51
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.92
Percentage of numeric attributes.
50568.45
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
20
The minimal number of distinct values among attributes of the nominal type.
0.08
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
20
The maximum number of distinct values among attributes of the nominal type.
2.8
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
16.91
Third quartile of skewness among attributes of the numeric type.
23.9
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
83.85
First quartile of kurtosis among attributes of the numeric type.
357114.78
Third quartile of standard deviation of attributes of the numeric type.
7792765.65
Maximum standard deviation of attributes of the numeric type.
1.93
Percentage of instances belonging to the least frequent class.
681.2
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
11
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
212.99
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
8.34
First quartile of skewness among attributes of the numeric type.
67437.29
Mean of means among attributes of the numeric type.
9417.27
First quartile of standard deviation of attributes of the numeric type.
0.7
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
4.2
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
157.41
Second quartile (Median) of kurtosis among attributes of the numeric type.
2.28
Number of attributes divided by the number of instances.
20
Average number of distinct values among the attributes of the nominal type.
13117.02
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
12.82
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
10.51
Percentage of instances belonging to the most frequent class.
343176.13
Mean standard deviation of attributes of the numeric type.
11.52
Second quartile (Median) of skewness among attributes of the numeric type.
60
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

87 tasks

11085 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
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