Author(s):
G. Venketeshwarlu, Kimmi Katoch, Shourya Pratap, Kapil Maurya, Amisha, Shweta Sharma, Shalini Thakur
Email(s):
Email ID Not Available
DOI:
10.52711/2349-2988.2026.00056 
Address:
G. Venketeshwarlu, Kimmi Katoch, Shourya Pratap, Kapil Maurya, Amisha, Shweta Sharma, Shalini Thakur
Arni School of Pharmacy, Arni University, Kathgarh, Indora, Kangra (Tehsil), Himachal Pradesh, India.
*Corresponding Author
Published In:
Volume - 18,
Issue - 4,
Year - 2026
ABSTRACT:
Millions of base pairs can be identified in a matter of hours thanks to Next Generation Sequencing (NGS) or deep sequencing technology, which allows for the parallel reading of numerous distinct DNA fragments. Recent studies have unequivocally demonstrated that machine learning technologies are capable of effectively analyzing massive genomic data sets and assisting in the discovery of new gene functions and areas of control. A collection of synthetic neurones that imitate the characteristics of real neurons makes up a deep artificial neural network. Artificial Neural Networks (ANN) are mathematical models that can be used to tackle artificial intelligence engineering challenges in several technical disciplines, such as proteomics, metabolomics, biology, and genomics. Neural networks are non-linear statistical structures arranged as modeling tools that are used to replicate intricate genetic interactions between inputs and outputs. Convolutional Neural Networks (CNNs). It has been shown that Recurrent Neural Networks (RNN) is the most effective methods for enhancing performance in genomic problem-solving tasks.
Cite this article:
G. Venketeshwarlu, Kimmi Katoch, Shourya Pratap, Kapil Maurya, Amisha, Shweta Sharma, Shalini Thakur. Role of Artificial Intelligence in Genome Analysis. Research Journal of Science and Technology. 2026; 18(4):393-9. doi: 10.52711/2349-2988.2026.00056
Cite(Electronic):
G. Venketeshwarlu, Kimmi Katoch, Shourya Pratap, Kapil Maurya, Amisha, Shweta Sharma, Shalini Thakur. Role of Artificial Intelligence in Genome Analysis. Research Journal of Science and Technology. 2026; 18(4):393-9. doi: 10.52711/2349-2988.2026.00056 Available on: https://www.rjstonline.com/AbstractView.aspx?PID=2026-18-4-12
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