Data Mining and Computational Intelligence in Genomics
Methods for Gene Expression Data Analysis
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The advent of efficient genome sequencing tools andhigh-throughput experimental biotechnology has leadto an enormous progress in life sciences. Among themost important innovations is the microarraytechnology, which allowed whole genome measurementsat transcriptional level. The characteristics of thisdata include a fair amount of noise and an atypicaldimensionality (which makes difficult the use ofclassic statistics tools experimental samples inthe order of dozens and measured parameters inthousands or tens of thousands). Therefore, this bookpresents a series of computational methods andalgorit...
The advent of efficient genome sequencing tools and
high-throughput experimental biotechnology has lead
to an enormous progress in life sciences. Among the
most important innovations is the microarray
technology, which allowed whole genome measurements
at transcriptional level. The characteristics of this
data include a fair amount of noise and an atypical
dimensionality (which makes difficult the use of
classic statistics tools experimental samples in
the order of dozens and measured parameters in
thousands or tens of thousands). Therefore, this book
presents a series of computational methods and
algorithms, capable of extracting valuable biological
knowledge from this type of data. Applications of
microarrays and subsequent gene expression analysis
range from the assignment of functional categories
for genes of unknown biological function, to precise
and early diagnosis of different tumor malignancies.
Besides these, a central goal of computational
analysis of gene expression data is the extraction of
regulatory knowledge at genetic level that may be
used to provide a broader understanding on the
functioning of complex cellular systems.
high-throughput experimental biotechnology has lead
to an enormous progress in life sciences. Among the
most important innovations is the microarray
technology, which allowed whole genome measurements
at transcriptional level. The characteristics of this
data include a fair amount of noise and an atypical
dimensionality (which makes difficult the use of
classic statistics tools experimental samples in
the order of dozens and measured parameters in
thousands or tens of thousands). Therefore, this book
presents a series of computational methods and
algorithms, capable of extracting valuable biological
knowledge from this type of data. Applications of
microarrays and subsequent gene expression analysis
range from the assignment of functional categories
for genes of unknown biological function, to precise
and early diagnosis of different tumor malignancies.
Besides these, a central goal of computational
analysis of gene expression data is the extraction of
regulatory knowledge at genetic level that may be
used to provide a broader understanding on the
functioning of complex cellular systems.
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