The Assistance of Applied Mathematics in Biomedical Sciences
Research Article
Open Access
CC BY

The Assistance of Applied Mathematics in Biomedical Sciences

Linyun Zhou 1*
1 Suzhou Science & Technology Town Foreign Language High School, Suzhou, Jiangsu, China, 215000
*Corresponding author: zhoulinyun20080802@163.com
Published on 20 July 2025
Volume Cover
TNS Vol.125
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-80590-233-1
ISBN (Online): 978-1-80590-234-8
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Abstract

This paper focuses on the facilitating role of applied mathematics in biomedical sciences. As an interdisciplinary field integrating biology and medicine, biomedical science faces challenges such as handling large amounts of data and integrating multiple disciplines in its development. Mathematics, statistics, and computational methods have played a significant role in this field. This paper finds that the SIR model in mathematical modeling has been instrumental in epidemic prevention and control, the NCC model has provided new ideas for cancer treatment, and the QSAR model has played a crucial role in drug development. Statistical principles such as regression analysis and variance analysis are indispensable in biomedical research. Computational methods also have extensive and critical applications in bioinformatics, medical imaging, systems biology, and other areas. These disciplines are interdependent, transforming raw biological data into actionable insights. Although there are challenges such as data integration, their continuous integration and technological advancements are expected to open up new frontiers in areas such as precision medicine and drive the future development of human healthcare.

Keywords:

Applied mathematics, Biomedical sciences, Mathematical modeling, Basic statistical principles, Computational methods

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Zhou,L. (2025). The Assistance of Applied Mathematics in Biomedical Sciences. Theoretical and Natural Science,125,60-64.

References

[1]. Lin, Zhigui. Four - tier response system and spatial propagation of COVID - 19 in China by a network model [J/OL].2020

[2]. Cheng, Wanqin. The NCC mathematical modeling framework for decision - making of six major career [J/OL].2022 - 11 - 20.

[3]. Fischer, Hans Peter. Mathematical modeling of complex biological systems: from parts lists to understanding systems behavior [J/OL]. 2008

[4]. Moore, Helen; Allen, Richard. What Can Mathematics Do for Drug Development? [J/OL]. 2019

[5]. Zhang, Jianting; Yang, Wenjian. A Comparison of the Efficacy of Botulinum Toxin Type A, Triamcinolone Acetonide, and Fractional CO₂Laser in the Treatment of Hypertrophic Scars: A Randomized Controlled Trial [J/OL]. 2024

[6]. Jumper J, et al. Highly accurate protein structure prediction with AlphaFold [J]. Nature, 2021

[7]. Sliwoski G, et al. Computational methods in drug discovery [J]. Chemical Reviews, 2014

[8]. Poplin R, et al. Prediction of breast cancer metastasis using deep learning on histopathology images [J]. Nature Medicine, 2018

[9]. Vabalas D, et al. COVID - 19 pandemic forecasting using mobility data and machine learning [J]. Scientific Reports, 2021

References

[1]. Lin, Zhigui. Four - tier response system and spatial propagation of COVID - 19 in China by a network model [J/OL].2020

[2]. Cheng, Wanqin. The NCC mathematical modeling framework for decision - making of six major career [J/OL].2022 - 11 - 20.

[3]. Fischer, Hans Peter. Mathematical modeling of complex biological systems: from parts lists to understanding systems behavior [J/OL]. 2008

[4]. Moore, Helen; Allen, Richard. What Can Mathematics Do for Drug Development? [J/OL]. 2019

[5]. Zhang, Jianting; Yang, Wenjian. A Comparison of the Efficacy of Botulinum Toxin Type A, Triamcinolone Acetonide, and Fractional CO₂Laser in the Treatment of Hypertrophic Scars: A Randomized Controlled Trial [J/OL]. 2024

[6]. Jumper J, et al. Highly accurate protein structure prediction with AlphaFold [J]. Nature, 2021

[7]. Sliwoski G, et al. Computational methods in drug discovery [J]. Chemical Reviews, 2014

[8]. Poplin R, et al. Prediction of breast cancer metastasis using deep learning on histopathology images [J]. Nature Medicine, 2018

[9]. Vabalas D, et al. COVID - 19 pandemic forecasting using mobility data and machine learning [J]. Scientific Reports, 2021

Cite this article

Zhou,L. (2025). The Assistance of Applied Mathematics in Biomedical Sciences. Theoretical and Natural Science,125,60-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Multi-Qubit Quantum Communication for Image Transmission over Error Prone Channels

ISBN: 978-1-80590-233-1(Print) / 978-1-80590-234-8(Online)
Editor: Anil Fernando
Conference website: https://2025.confapmm.org/
Conference date: 29 August 2025
Series: Theoretical and Natural Science
Volume number: Vol.125
ISSN: 2753-8818(Print) / 2753-8826(Online)

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