Donated on 8/11/2013

A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.

Dataset Characteristics


Subject Area

Life Science

Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional 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 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, labelled A, B, C and D, involved species that belong to the same genus, * 2 mixtures, labelled E and F, involved species that belong to distinct genera, but to the same Gram type, * 4 mixtures, labelled 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.

Has Missing Values?


Variable Information

Provide information about each attribute in your data set.

0 citations


Pierre Mah

Jean-Baptiste Veyrieras


By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy