AI Enhanced Bioinformatics Pipelines in DevOps

Yogesh Ramaswamy

doi:10.71097/IJSAT.v16.i1.2136

AI Enhanced Bioinformatics Pipelines in DevOps

Author(s)	Yogesh Ramaswamy
Country	India
Abstract	The implementation of AI methods into DevOps pipelines within bioinformatics has several effects: Initially, it liberates researchers’ time as it provides for automation of simple and repetitive procedures like data preprocessing and feature extraction. Secondly deployment of AI models can be done at scale as the data can be processed in parallel and distributed fashion. This helps to enhance the rate of processing large amounts of information and the efficiency of functioning. In addition, the integration of AI into the models has been proven to increase the high predictive and classifying effectiveness of bioinformatics results gained from specimens. This paper also showed that AI with aspects of DevOps exists in the field of bioinformatics, and they aid in such approaches’ fine working. For example, in the process of protein-protein interactions, gene functions and diseases, machine learning models have been employed to give reliable forecasts. Machine learning algorithms such as deep learning have been applied to find disease subtypes from genomic data, hence leading to precision medicine. Now, due to NLP, great knowledge from huge amounts of scientific literature can be effectively exploited to generate new biological hypotheses. However, there are issues that are yet to be solved with regard to this aspect of research findings. The most evident one can be named as the absence of definite procedures and methods for the proper incorporation of AI into DevOps frameworks in bioinformatics. More importantly, there is a need to standardize practice to allow the replication of findings and increase the visibility of the analysis that is done through the use of Artificial Intelligence. Also, bioinformatics is an active area of research which experiences the setting of new technologies and data types regularly. This construes a need for constant invention and interdisciplinary efforts, especially at the intersection in-between biology, computer science, and statistics
Keywords	Artificial Intelligence, Bioinformatics, DevOps, Machine Learning, Deep Learning, Data Analysis, Pipelines, Scalability, Reproducibility.
Field	Engineering
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-07
Cite This	AI Enhanced Bioinformatics Pipelines in DevOps - Yogesh Ramaswamy - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2136
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2136
Short DOI	https://doi.org/g87cwc

View / Download PDF File

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4 Cover Page Vol 15 Isu 3 Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI Enhanced Bioinformatics Pipelines in DevOps

Share this