A neural
network is a massively parallel distributed processor made up of single
processing units that has a natural propensity for storing experimental
knowledge and making it available for use. It resembles the brain in two
respects:
1.
knowledge is acquired by the network from its environment through a learning
process:
2.
Interneuron connection strengths, known as synaptic weights, are used to store
the acquired knowledge.
Source: Simon Haykin, Neural
networks and learning machine, third edition, Pearson Education, Inc, 2009.
Neural Networks have been applied
widely to real problem modeling. The applications have been concentrated mainly
on image processing, classification of patterns, and function approximation.
The best characteristic of neural networks from the point of view for function
approximation is the parallel processing power. Besides neural networks by its
own are just linear models, their local structure for calculation, named
neurons, may be selected properly in order to those behave as a nonlinear
model. In the other hand, Transcription Networks has been applied as a gene
expression modeling tool. The principle behind those is that nature solves
problems in “local machines” termed network motifs. Here is claimed that the
theory of neural networks is a powerful tool for transcription network modeling
based on experimental data from gene expression.
All the living
organism physical state is function in a production and replication level of
the genetic code, including all the biological fluids and internal structures.
One may use applied genetics to explain biological components, such as the
blood cells or different biological liquid separators, which in wrong
development or repairing processing may be the cause of significant physical
diseases. This justifies the study of genetics in the gene-level.
Key-words: Neural Networks;
Transcription Networks; Gene Expression Estimation; Systems Biology.
Schematic view of how a typical supervised neural network handles the data for learning
Schematic view of the backpropagation, two waves of information, backward and forward
Abstract—Systems biology is a potential new branch of biology with roots in
many other fields such as biology, physics, mathematics, and computer science. Accordingly,
this is a manifestation of mathematical biology and endeavors to understand
complex systems, such as networks of genes, as interacting systems’
components. In this paper, we analyze
the field from a literature review framework; the author is passive in the
review. Further, we conclude that systems biology is a potential field for
contributing to the new horizons of science, especially biomedical sciences;
also the weakness of systems biology is that it is not commonly mentioned in
traditional books in biology, even recently published. Nonetheless, as the
literature claims, this field might be the bridge for making biology,
especially genetics, a source of ideas and principles for other fields, such as
biomechanics; and simultaneously an example for other fields of biomedical
sciences that mathematics, physics and other branch indeed can enhance those
with new insights and methods.
