New Sights of Mathematical Modeling in Biomedicine: Updates and Direction
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 143
Special Issue Editor
Interests: mathematical modeling; biomedicine; computational epidemiology; biostatistics; disease ecology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, mathematical modeling and machine learning have spread to almost all fields. This Special Issue, "New Sights of Mathematical Modeling in Biomedicine: Updates and Direction", focuses on the latest advancements and future directions in mathematical modeling applied to the field of biomedicine. This issue brings together a collection of research papers and studies that highlight the emerging trends, innovative techniques, and novel applications of mathematical modeling in understanding and improving various aspects of biomedicine.
It begins with an overview of the current state of mathematical modeling in biomedicine, discussing its strengths, limitations, and potential for future growth. Subsequent papers delve into specific areas of research, such as the modeling of gene regulatory networks, mathematical optimization for personalized cancer treatment, and the use of machine learning algorithms in predicting disease outcomes. Furthermore, this issue explores the integration of mathematical modeling with other scientific disciplines, such as genomics, proteomics, and systems biology, to create a comprehensive framework for understanding and studying biological systems. The interdisciplinary nature of these studies highlights the importance of collaboration between mathematicians, biologists, clinicians, and computational scientists in advancing the field of biomedicine.
Overall, this Special Issue provides a valuable update on the latest developments in mathematical modeling in biomedicine and offers insights into the future directions of research in this field. It serves as a comprehensive resource for researchers, students, and professionals interested in utilizing mathematical modeling to enhance our understanding of biological processes and improve healthcare outcomes.
Dr. Kayode Oshinubi
Guest Editor
Manuscript Submission Information
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Keywords
- mathematical modeling
- machine learning
- predictive modeling
- data modeling
- biological systems
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