The entirety of the genetic information contained in an organism's DNA (deoxyribonucleic acid) is referred to as genomic data. Numerous experimental methods, such as chromatin immunoprecipitation (ChIP), microarray analysis, DNA sequencing, and high-throughput sequencing technologies like RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq), are used to collect genomic data.
Published in Chapter:
Introduction to AI in Biomedical and Biotechnology
R. K. Chaurasia (The ICFAI University, Jaipur, India), Vaibhav Maheswari (The ICFAI University, Jaipur, India), and
A. K. Saini (The ICFAI University, Jaipur, India)
Copyright: © 2024
|Pages: 20
DOI: 10.4018/979-8-3693-3629-8.ch002
Abstract
The infusion of biomedical and bio-technology is gaining high visibility as an asset for estimating various health problems at a fast pace as well as making it less expensive than the earlier methodologies. Despite the challenges, AI is very helpful in the near future in many ways such as early detection and diagnoses of a disease, providing more effective and personalized treatment options, reducing the healthcare cost, and improving the resource allocation. AI algorithms are also being used to analyze x-rays, CT scans, and other images to detect disease earlier with a great accuracy which leads to improved health outcomes of the patient. AI also analyzes massive genomic data to recognize infected genes, predict infection risk, and develop personalised therapies. By this, it can be concluded that the infusion of AI in biomedical and biotechnical has pushed healthcare into a transformative era.