Computational and Analytic Methods in Biological Sciences: Bioinformatics with Machine Learning and Mathematical Modelling

Cancer is the second-leading cause of death worldwide, and despite advances in healthcare, it remains a significant challenge. Mathematical modelling and simulation are viable alternatives to experiments in medicine and biology, and this book explores the probabilistic computational deep learning model for cancer classification and prediction.

Format: Hardback
Length: 296 pages
Publication date: 31 May 2023
Publisher: River Publishers

Despite significant advancements in healthcare over the past century, the successful treatment of cancer remains a formidable challenge, ranking as the second leading cause of death worldwide, trailing only cardiovascular disease. Early detection and survival are paramount in combating this disease. The development of quantitative methods and computer technology has played a pivotal role in shaping new models in medical and biological sciences. The application of mathematical modelling in solving complex real-world problems in medicine and biology has yielded remarkable results.

However, it is important to note that despite advancements in instrumentation technology and biomedical equipment, conducting experiments in medicine and biology can be challenging for various reasons. As a result, mathematical modelling and simulation are considered viable alternatives in such situations. This book delves into the realm of mathematical modelling and simulation, providing a comprehensive exploration of their applications in medicine and biology.

Conventional diagnostic techniques for cancer often rely on physical and morphological characteristics of the tumor, making early stage prediction and diagnosis challenging. It is widely recognized that cancers involve genome-level changes, making the prognosis of various cancer types dependent on data generated through diverse experiments. While machine learning techniques exist for analyzing the data of expressed genes, recent advancements in deep learning algorithms have proven to be more accurate and adaptable, particularly in selecting and classifying informative genes.

This book aims to present a probabilistic computational deep learning model for cancer classification and prediction, leveraging the power of advanced algorithms to analyze complex gene expression data and make informed predictions about cancer outcomes. By exploring the potential of mathematical modelling and simulation in medicine and biology, this book aims to contribute to the ongoing efforts in combating cancer and improving patient care.

Weight: 658g
Dimension: 162 x 241 x 21 (mm)
ISBN-13: 9788770226950