Open Access Review

Unravelling the application of machine learning in cancer biomarker discovery

by Carter William a,* Choki Wangmo a  and  Anjali Ranjan b
a
James Cook University, Brisbane, Australia
b
Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India
*
Author to whom correspondence should be addressed.
CI  2023, 15; 2(1), 15; https://doi.org/10.58567/ci02010001
Received: 4 May 2023 / Accepted: 18 May 2023 / Published: 14 June 2023

Abstract

Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.


Copyright: © 2023 by William, Wangmo and Ranjan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
William, C.; Wangmo, C.; Ranjan, A. Unravelling the application of machine learning in cancer biomarker discovery. Cancer Insight, 2023, 2, 15. https://doi.org/10.58567/ci02010001
AMA Style
William C, Wangmo C, Ranjan A. Unravelling the application of machine learning in cancer biomarker discovery. Cancer Insight; 2023, 2(1):15. https://doi.org/10.58567/ci02010001
Chicago/Turabian Style
William, Carter; Wangmo, Choki; Ranjan, Anjali 2023. "Unravelling the application of machine learning in cancer biomarker discovery" Cancer Insight 2, no.1:15. https://doi.org/10.58567/ci02010001
APA style
William, C., Wangmo, C., & Ranjan, A. (2023). Unravelling the application of machine learning in cancer biomarker discovery. Cancer Insight, 2(1), 15. https://doi.org/10.58567/ci02010001

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