Biomedical Engineering Reference
In-Depth Information
1 Introduction
The genetic algorithm (GA) was
rst introduced by Holland and his team in the
university of Michigan in the early 1960s [
1
]. It is widely used to solve many
optimization problems in all
elds of engineering and sciences. These algorithms
are hypothetically and empirically proved to be giving ef
cient search in complex
space. GAs are very useful, particularly when the size of the dataset is very large.
Knowledge engineering can be used to solve a diversity of tasks like clustering,
classi
cation, and regression and association discovery [
2
]. Association rule mining
is one of the widely used approaches for constructing the computational intelligence
techniques. A classi
cation rule is represented in the form if P then Q, where P is a
combination of predicting attribute values and Q is the predicted class.
Basheer et al. [
3
] proposed a GA-based approach for mining classi
cation rules
from large database is presented for emphasizing accuracy, coverage, and com-
prehensibility of the rules and simplifying the implementation of a GA. The design
of encoding, genetic operators, and
tness function of GA for this task are dis-
cussed. Experimental results show that GA proposed in this paper is suitable for
classi
cation rule mining and those rules discovered by the algorithm have higher
classi
cation performance to unknown data.
Pradhan et al. [
4
] proposed a multiclass genetic programming (GP)-based
classi
er design that will help the medical practitioner to con
rm his/her diagnosis
toward prediabetic, diabetic, and non-diabetic patients.
Permann [
5
], this paper tell us about initial research using GAs his help to
optimize infrastructure protection and restoration decisions. This research suggests
that it applies GAs for problem infrastructure modeling and observes in order to
determine the optimum assets to restore other disaster. First, the problem space is
introduced. Next, the change based on simulation used by the GAs is introduced.
Then, the critical subnetwork concept, in GAs, and similar research are described.
Finally, the GA for decision-making research is discussed.
Neurokinin 1 (NK1) is within a receptor for substance P. Substance P involved
in pain transmission. NK1 is also known as TACR1 gene.
The neurokinin (NK) 1 receptor is a G protein-coupled receptor and member of
the tachykinin family, which also includes NK2 and NK3 receptors. The NK1
receptor is localized in highly concentrated central nervous system (CNS) and
particularly the striatum, and some hypothalamic and thalamic nuclei and peripheral
tissues and uncureable renal, liver, and skin problems. NK1 receptors do not act
directly to the central stress reactions, mood control, excitatory neurotransmission,
immune modulation, and airway and lung function. The receptor displays greater
potency for the endogenous agonist substance P than for neurokinin A and neu-
rokinin B (SP > NKA > NKB). The human gene encoding the NK1 receptor has
been localized.
TACR1 (tachykinin receptor 1 or neurokinin receptor 1) is a coding gene.
Diseases associated with TACR1 include telogen ef
uvium, liver, skin problems,
and peripheral nervous problems; these are related to super pathways: GPCR ligand
fl
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