Package for the detection of molecules in complex mixtures
of compounds. It creates an initial_DATA matrix from several
GC-MS analyses by collecting and assembling the information
from chromatograms and mass spectra (\emph{MS.DataCreation}).It
tests for the best unsupervised clustering method to group
similar mass spectra into molecules (\emph{MS.test.clust}).It
runs the optimal unsupervised clustering method on the
initial_DATA matrix, identifies the optimal number of clusters,
produces different files for facilitating the quality control
and identification of putative molecules, and returns
fingerprinting or profiling matrices (\emph{MS.clust}).It
converts output files from \emph{MS.clust} for NIST mass
spectral library search and ARISTO webtool search

Elodie Courtois <courtoiselodie@gmail.com>, Yann Guitton
<yann.guitton@gmail.com>, Florence Nicole
<florence.nicole@univ-st-etienne.fr>
Package for the detection of molecules in complex mixtures
of compounds. It creates an initial_DATA matrix from several
GC-MS analyses by collecting and assembling the information
from chromatograms and mass spectra (MS.DataCreation), It can
read several format (ASCII, CDF, mzML, mzXML or mzData).It
tests for the best unsupervised clustering method to group
similar mass spectra into molecules (MS.test.clust).It runs the
optimal unsupervised clustering method on the initial_DATA
matrix, identifies the optimal number of clusters, produces
different files for facilitating the quality control and
identification of putative molecules, and returns
fingerprinting or profiling matrices (MS.clust).It converts
output files from MS.clust for NIST mass spectral library
search and ARISTO webtool search

A Tcl/Tk GUI for some basic functions in the MSeasy
package

Yann Guitton <yann.guitton@gmail.com> Cyrille Conord
<conord@emse.fr>

c(
person("Matineh", "Rahmatbakhsh", , "matinerb.94@gmail.com", c("aut", "cre")))
The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.