This book constitutes the refereed proceedings of the Second International Symposium on Mathematical and Computational Oncology, ISMCO 2020, which was supposed to be held in San Diego, CA, USA, in October 2020, but was instead held virtually due to the COVID-19 pandemic. The 6 full papers and 4 short papers presented together with 1 invited talk were carefully reviewed and selected from 28 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; general cancer computational biology; and posters.

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.

Cancer is a complex adaptive dynamic system that causes both local and systemic failures in the patient. Cancer is caused by a number of gain-of-function and loss-of-function events, that lead to cells proliferating without control by the host organism over time. In cancer, the immune system modulates cancer cell population heterogeneity and plays a crucial role in disease outcomes. The immune system itself also generates multiple clones of different cell types, with some clones proliferating quickly and maturing into effector cells. By creating regulatory signals and their networks, and generating effector cells and molecules, the immune system recognizes and kills abnormal cells. Anti-cancer immune mechanisms are realized as multi-layer, nonlinear cellular and molecular interactions. A number of factors determine the outcome of immune system-tumor interactions, including cancer-associated antigens, immune cells, and host organisms.

Strategies for Overcoming Chemotherapy Resistance in Cervical Cancer: From Molecular Insights to Precision Solutions, Volume 21 highlights different strategies to reverse chemotherapy resistance in cervical cancer. The book puts a strong focus on strategies to reverse chemotherapy resistance as well as strategies for early detection of the resistance, enhancing precision oncology in terms of patient care and maximizing patient management. The book also looks at virally induced resistance to chemotherapy and recommends combination therapies that can maximize the reversal of this resistance.In 10 chapters, the book not only gives an overview of cervical cancer and chemotherapy as treatment, but also investigates resistance to chemotherapy and treatment for resistance. It defines treatment mechanisms, options, and limitations to beat chemotherapy resistance and the reversal of the resistance mechanisms. It gives insights into future directions of cervical cancer treatment using epigenetic silencing, chemotherapy splicing, the involvement of MicroRNAs to chemotherapy resistance, and the application of Artificial Intelligence. - Discusses strategies to reverse and detect resistance to chemotherapy at an early stage - Investigates the applications of Artificial Intelligence in the study of cervical chemotherapy resistance - Presents research and applications developed to overcome cancer resistance

The study of complex systems and their related phenomena has become a major research venue in the recent years and it is commonly regarded as an important part of the scientific revolution developing through the 21st century. The science of complexity is concerned with the laws of operation and evolution of systems formed by many locally interacting elements that produce collective order at spatiotemporal scales larger than that of the single constitutive elements. This new thinking, that explores formally the emergence of spontaneous higher order and feedback hierarchies, has been particularly successful in the biological sciences. One particular life-threatening disease in humans, overwhelmingly common in the modern world is cancer. It is regarded as a collection of phenomena involving anomalous cell growth caused by an underlying genetic instability with the potential to spread to other parts of the human body. In the present book, a group of well recognised specialists discuss new ideas about the disease. These authors coming from solid backgrounds in physics, mathematics, medicine, molecular and cell biology, genetics and anthropology have dedicated their time to write an authoritative free-available text published under the open access philosophy that hopefully would be in the front-line struggle against cancer, a complex disease.