remote genetic change contributes to an epigenetic change.
e, various regression algorithms which can rank variables will be
d in this chapter. How they work for the genetic-epigenetic
pattern discovery problem will also be demonstrated using
in this chapter.
er 5 will focus on the problem of discovering the chemicals or
s which reside on a spectra curve. This is a very special data type.
e on which the chemicals or molecules reside is called a baseline
rmally non-analytic and complex. To discover the chemicals or
cules, it is required to estimate and remove this baseline as
y as possible. Therefore, various spectra baseline estimation
ms will be introduced in this chapter. How these algorithms can be
support the robust chemical or molecule discovery will be
ated in this chapter as well. In addition, aligning multiple spectra
mprove the discovery quality will also be discussed.
er 6 will focus on another responsive gene discovery problem.
d of research requires to discover the differentially expressed
an experiment. There are two types of data for this kind of
ons, i.e., the microarray gene expression data and the sequencing
a. Different methods for dealing with these two types of data will
uced in this chapter. In addition to commonly used algorithms,
ter will also introduce the algorithms tailored to special data used
vering differentially expressed genes. For instance, this chapter
duce algorithms which are designed to handle the problems of
iscover the differentially expressed genes for a data set in which
re presented, how to discover the subpopulation formation, i.e.,
dal genes and how to discover the differentially expressed genes
set with insufficient replicate.
er 7 will focus on the whole-genome pattern discovery problem.
sical and advanced sequence comparison approaches will be
d in this chapter. A typical problem will be used for this chapter,
to discover the pattern of the SARS-CoV-2 pandemic data in
the genetic diversity across countries. A key question is whether
genomes from different countries demonstrate a significant