4th EucheMs chemistry congress

thursday, 30-Aug 2012
s615
chem. Listy 106, s587–s1425 (2012)
Analytical chemistry Electrochemistry, Analysis, sample manipulation
Chemometrics – ii
o - 4 2 1
fuSion of MetABoLoMiCS dAtA for A Better
underStAndinG of MuLtiPLe SCLeroSiS
L. BuydenS 1
1 Radboud University Nijmegen, IMM, Nijmegen, Netherlands
While Multiple sclerosis is a major disabling disease of the
Central nervous System (CNS) in young adults, little is known on
the real cause of this disease; Even diagnosis in an early stage is
a non-solved issue.Cerebrospinal Fluid (CSF) is the bio fluid,
which is in closest interaction with the Central Nervous System
(CNS). It is therefore the bio fluid that best mirrors the
biochemical status and processes in brain and CNS. Biochemical
changes are therefore most likely to be found by means of a
comprehensive analysis of the CSF Other bio fluids such as
plasma may also contain crucial information;
Comprehensive analysis by a large variety of analytical
technologies, yield however complex data for which chemometric
data analysis and data mining have become crucial tools. Since
no analytical platform on its own yields a comprehensive image
of the biochemical status, data fusion has become widespread in
the last decade. Many methods have been proposed, most of them
restrict to a linear fusion strategy However, it is not realistic to
assume that all biological or (bio)chemical data display this simple
linear behavior. In that case linear methods are bound to fail. In
this lecture alternative approaches will be presented. One is based
on the hierarchical fusion of mid-level fusion models. Non-linear
kernel fusion model allow to cope specifically with
nonlinearities. [1] We use our pseudo-sample approach [2, 3] to reveal
the contribution of the individual variables.
In the lecture we will present results of fusion of CSF and
plasma analysis data for a better diagnosis and search for
biomarkers for Multiple Sclerosis
references:
1. S. Yu et al., Kernel-based Data Fusion for Machine
Learning. Methods and applications in Bioinformatics and
Text mining. Springer: Berlin 2011.
2. P. Krooshof et al., Analytical Chemistry 82 (2010)
7000–7007
3. Postma et al. Analytica Chimica Acta 705 (2011) 123–134
Keywords: data fusion; metabolomics data analysis; multiple
sclerosis; chemometrics;
Chemometrics – ii
4 th EucheMs chemistry congress
o - 4 2 2
SiMuLtAneouS SiGnifiCAnt fACtor deteCtion
And vAriABLe SeLeCtion uSinG MuLti-BLoCK
AnALySiS MethodS
d. rutLedGe 1
1 AgroParisTech/INRA, UMR 1145 “Génie industriel
Alimentaire”, Paris, France
Multivariate methods are nowdays widely used in the study
of data matrices containing thousands or hundreds of thousands
of variables. In spite of their ability to work with many correlated
and noisy variables and to separate the significant variation from
the noise, these methods can still be improved by a relevant
selection of variables.
In this presentation, we propose to split the data variablewise
into a certain number of segments, each considered as an
individual block of data, and to detect the relevant segments with
a Multiple-Block method.
In the case of data sets where the values of the variables are
thought to vary with the levels of experimental factors, it is
possible simultaneously to detect which factors have a significant
effect.
Application of the proposed methods to several data sets,
increasing in complexity, has shown satisfactory results.
Keywords: Variable Selection; Factor detection;
AUGUst 26–30, 2012, PrAGUE, cZEcH rEPUbLIc