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.
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