Computational Intelligence in Oncology: Applications in Diagnosis, Prognosis and Therapeutics of Cancers

This book highlights recent applications of computational intelligence (CI) methods in the field of computational oncology, focusing on cancer diagnosis, prognosis, and optimized therapeutics. CI methods, such as artificial neural networks, fuzzy logic, evolutionary computations, machine learning, and deep learning, are used to understand the hallmarks of cancer development, progression, and effective therapeutics. The book aims to provide state-of-the-art applications of CI methods derived from core computer sciences to back medical oncology, including multi-omics exploration, gene expression analysis, gene signature identification, genomic characterization, anti-cancer drug design, and drug response prediction.

Format: Hardback
Length: 467 pages
Publication date: 02 March 2022
Publisher: Springer Verlag, Singapore

The cancer, a complex and diverse disease, is categorized into various subtypes, according to the World Health Organization's (WHO) latest report. In 2020, cancer claimed the lives of over 10 million people, making it a leading cause of death worldwide. As a result, early diagnosis, prognosis, and classification of cancer to specific subtypes have become crucial for effective clinical management and the development of personalized therapeutics. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning algorithms, and nature-inspired techniques, have been extensively employed in various aspects of oncology research, including cancer diagnosis, prognosis, therapeutics, and optimized clinical management.

Significant advancements have been made in understanding the hallmarks of cancer development, progression, and the development of effective therapeutics. However, despite the presence of extrinsic and intrinsic factors contributing to the rising incidence of cancer cases, the detection, diagnosis, prognosis, and therapeutics remain major challenges for the medical community. The advent of CI-based approaches, including nature-inspired techniques, and access to clinical data from high-throughput experiments has provided renewed hope for developing and implementing CI in various aspects of oncology.

The primary objective of this book is to showcase state-of-the-art applications of CI methods that have been derived from core computer sciences and applied to support medical oncology. The book encompasses chapters on artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary computations, machine learning and deep learning algorithms, and nature-inspired techniques. Each chapter presents detailed discussions on the latest research findings, theoretical frameworks, and practical applications in cancer diagnosis, prognosis, therapeutics, and optimized clinical management.

By presenting a comprehensive overview of CI methods in oncology, this book aims to provide a valuable resource for medical consultants, researchers, and oncologists. It serves as a platform for exchanging knowledge, fostering collaborations, and driving innovation in the field of computational oncology. The editors have assembled a team of experts from leading institutions worldwide, ensuring that the content is up-to-date, comprehensive, and relevant to the current challenges and opportunities in cancer research and treatment.

In conclusion, this book encapsulates recent applications of CI methods in the field of computational oncology, particularly in cancer diagnosis, prognosis, and its optimized therapeutics. With its multidisciplinary approach, the book offers a comprehensive understanding of the latest advancements in CI and their potential to transform cancer research and treatment. By highlighting the state-of-the-art applications of CI methods, this book provides a valuable resource for medical professionals, researchers, and oncologists, contributing to the advancement of personalized medicine and improved patient outcomes in the fight against cancer.

Weight: 893g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811692208
Edition number: 1st ed. 2022