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biomarker discovery process by making sense of
data with respect to biological mechanisms. It
includes proper preprocessing and pretreatment
of the raw MS data to allow valid subsequent
modeling. The obtained models are expected to
offer meaningful insights into speci
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c cellular
or disease processes by summarizing the data
set and extracting its relevant characteristics.
Finally, biologically pertinent biomarkers can
be highlighted from the models to describe the
phenomenon under study with respect to prior
knowledge.
An increasing number of studies involve
several analytical setups to provide a broader
coverage of complex samples. Such experiments
lead to the generation of multiple
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data
sets and integrative analyses will acquire
a growing importance in the perspective of
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Data fusion will also in
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discovery with the monitoring of regulatory
networks related to several biological layers
such as transcripts, proteins, and metabolites.
Great challenges are foreseen in terms of data
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modeling, and dimensionality reduction for
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