This book encapsulates recent applications of CI methods in the field of
computational oncology, especially cancer diagnosis, prognosis, and its
optimized therapeutics.
The cancer has been known as a heterogeneous disease categorized in
several different subtypes. According to WHO's recent report, cancer is
a leading cause of death worldwide, accounting for over 10 million
deaths in the year 2020. Therefore, its early diagnosis, prognosis, and
classification to a subtype have become necessary as it facilitates the
subsequent clinical management and therapeutics plan. Computational
intelligence (CI) methods, including artificial neural networks (ANNs),
fuzzy logic, evolutionary computations, various machine learning and
deep learning, and nature-inspired algorithms, have been widely utilized
in various aspects of oncology research, viz. diagnosis, prognosis,
therapeutics, and optimized clinical management.
Appreciable progress has been made toward the understanding the
hallmarks of cancer development, progression, and its effective
therapeutics. However, notwithstanding the extrinsic and intrinsic
factors which lead to drastic increment in incidence cases, the
detection, diagnosis, prognosis, and therapeutics remain an apex
challenge for the medical fraternity. With the advent in CI-based
approaches, including nature-inspired techniques, and availability of
clinical data from various high-throughput experiments, medical
consultants, researchers, and oncologists have seen a hope to devise and
employ CI in various aspects of oncology. The main aim of the book is to
occupy state-of-the-art applications of CI methods which have been
derived from core computer sciences to back medical oncology. This
edited book covers artificial neural networks, fuzzy logic and fuzzy
inference systems, evolutionary algorithms, various nature-inspired
algorithms, and hybrid intelligent systems which are widely appreciated
for the diagnosis, prognosis, and optimization of therapeutics of
various cancers. Besides, this book also covers multi-omics exploration,
gene expression analysis, gene signature identification of cancers,
genomic characterization of tumors, anti-cancer drug design and
discovery, drug response prediction by means of CI, and applications of
IoT, IoMT, and blockchain technology in cancer research.
