The Role of DNA Microarrays and Machine Learning in Cancer Research

Author:

Agrawal Arun1ORCID,Gupta Deepak1ORCID,Tomar Archana1ORCID,Bhargava Chandra Praksah1ORCID,Shrivastava Deshdeepak1ORCID

Affiliation:

1. Institute of Technology and Management, Gwalior, India

Abstract

This chapter explores the pivotal intersection of DNA microarrays and machine learning within the area of cancer research. It underscores the principal significance of gene expression profiling as a cornerstone for precise cancer diagnosis and the development of personalized treatment protocols. This chapter thoroughly examines the intricacies inherent in gene expression data analysis, encompassing challenges related to noise and the formidable task of dealing with a vast array of genes in relation to the limited available samples. Furthermore, it highlights the transformative potential of machine learning in propelling the evolution of oncological decision support systems, providing clinicians and researchers with the tools to make informed, data-driven decisions. In summary, this chapter elucidates how the synergistic integration of DNA microarrays and machine learning is reshaping the landscape of cancer research and healthcare, offering a promising future characterized by heightened diagnostic accuracy and tailored therapeutic interventions.

Publisher

IGI Global

Reference58 articles.

1. IoT-based healthcare-monitoring system towards improving quality of life: A review.;SAbdulmalek;Health Care,2022

2. Aziz, R. M., & Verma, N. K. (2023). Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review. Human-Centric Intelligent Systems, 1-28.

3. BaldiP.HatfieldG. W. (2011). DNA microarrays and gene expression: from experiments to data analysis and modeling. Cambridge University Press.

4. Bancroft, J. D., & Gamble, M. (Eds.). (2008). Theory and practice of histological techniques. Elsevier health sciences.

5. Gene Expression Profiling of Breast Cancer

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3