Page 272 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
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Chapter 9 Applications of deep learning in biomedical engineering 263
differentiation, development, and adaptability of any living or-
ganism; the amount of a particular protein in a cell indicates
the balance between that protein's synthetic and degradative
biochemical pathways. The steps involved in gene expression
process include transcription, RNA splicing (pre-mRNA to
mRNA), translation (RNA to protein), and posttranslational modi-
fication (formation of mature protein) [27]. DL can be employed
in gene expression regarding two perspectives:
• Alternative splicing
• Prediction of gene expression [2]
36. Alternative splicing
It is also known as alternative RNA splicing or differential
splicing. It is a controlling process during gene expression.
In this mechanism, exons of a gene can be incorporated or
rejected in cis-acting regulatory elements of mRNA sequence,
which involves production of two or more isoforms [28].
37. Gene expression prediction
Gene expression prediction is a biological tool used to deter-
mine the reaction of cells to characterize the diseases and drug
treatments. Histone modifications play a significant task in
changing gene regulation activities. Predicting the histone modi-
fication helps to identify gene expression level (high or low) [2].
38. Genomic sequencing
Genomic sequencing replaces the traditional method of
sequencing individual genes one by one. With the advancement
of technology, sequencing the entire genome of an organism is
possible. It helps to explore the behavior of all the genes in a
cell. The chief resolution map of a genome is a nucleotide
sequence, which imparts complete information about genes.
The function of gene is controlled by many other regulatory
bodies such as response elements, enhancers, the silencers, and
so on [29].
The application of the DL in the genomic sequencing includes
two fields:
1. Learning the functional activity of DNA sequencing
2. DNA methylation [2]