Page 270 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

Chapter 9 Applications of deep learning in biomedical engineering 261

31. Omics

The concept of omics is the study of measuring relationships
and functions of various molecules such as genes, proteins, and
metabolites, which helps form the cell of an organism. Technol-
ogies that end with omics are as follows:
• Genomics is the study of function, evolution, and characteriza-
tion of genes. It involves creation of proteins using enzymes
and messenger molecules.
• Proteomics is the study of proteins in a cell or organism such as
proteineprotein interaction (PPI), quantification of proteins,
and so on.
• Metabonomics is the study of metabolic states of cell, organ, or
organisms. It is used to analyze biological fluids and tissue
types to identify the disease states. It generally uses high-
resolution analysis.
• Transcriptomics is the study of the RNA such as mRNA, nonen-
coding RNA, rRNA, and tRNA.
• Glycomics is the study of cellular carbohydrates.
• Lipomics is the study of cellular lipids [23].

32. Around the genome

In the recent days, DL plays a vital role in genomics to develop
new hypothesis. It utilizes genomic sequences as input and pre-
dicts genetic variations [24].The application of genome can be
categorized into three different fields such as follows:
1. Protein-binding prediction (PBP)
2. Gene expression
3. Genomic sequencing [2]
The DL implementation in genome is shown in Fig. 9.10.

33. Protein-binding prediction

PBP plays a significant role in biomolecular activities such as
DNA replication, regulation, signaling cellular pathways, and so
on. It acts a key role in regulating binding mechanisms.
Protein-binding interaction is calculated using two different
techniques:
• Surface plasmon resonance
• Isothermal titration calorimetry [25]