Bioinformatics Tools And Web Server For Cancer Biomarker Development

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The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics.

This book discusses the application of bioinformatics in cancer disease management. It covers general aspects of cancer as a disease but also as a success story in the translation of omics data in clinical settings. It provides an overview of the specific applications of bioinformatics tools in cancer epidemiology, prevention, and screening and in the identification of novel genetic and molecular biomarkers involved in cancer development. This is accomplished through the inclusion of numerous examples of the use of bioinformatics in precision oncology.

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.

Tools, techniques, and progress in cancer biomarkers discovery The completion of a number of gene sequencing projects, recent advances in genomic and proteomic technologies, and the availability of powerful bioinformatics tools have led to promising new avenues and approaches in the search for cancer biomarkers. This book provides a comprehensive overview of current methodologies and technologies. It discusses biomarker discovery as a whole, rather than focusing on one specific marker or cancer. With information on both existing and potential biomarkers, Cancer Biomarkers: Analytical Techniques for Discovery: * Provides insights into the current technological platforms for biomarker discovery, including mass spectrometry combined with multidimensional chromatography, DIGE, and various chip technologies * Includes a detailed discussion of protein networks and protein phosphorylation in cancer * Details the use of imaging mass spectrometry, laser capture microdissection, serial analysis of gene expression, enzyme-linked immunosorbent assays, protein microarrays, antibody-based microarrays, and bioinformatics * Covers the emerging role of surface-enhanced laser desorption ionization (SELDI) and various tagging and labeling strategies * Discusses related regulatory and ethical issues With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this is a core reference for biomarker researchers, scientists working in proteomics and bioinformatics, pharmaceutical scientists, oncologists, biochemists, biologists, and chemists.

Offering focused, practical, and up-to-date content on immunotherapies used to treat a wide variety of cancer types, Clinical Immuno-Oncology covers the basic cellular and molecular mechanisms involved in cancer initiation and progression. The text reviews the development of tumor specific antigens and the challenges of developing cancer immunotherapies. Every effort is made to relate the fundamental aspects of host immunity to the efficacy of current immunotherapies and how they work in conjunction with the immune system to combat cancer. This foundational resource explains the basics of cancer immunotherapy, discusses best practices, and provides recommendations from experienced clinicians in the field. Dr. John E. Niederhuber and a team of expert contributing authors provide clinically focused information essential for integrating these new therapies into practice—making this an ideal resource for fellows, practitioners, and other cancer team members in medical oncology, radiation oncology, cancer surgery, immunology, and cancer research. Offers balanced and synthesized content on clinical science, clinical and translational research, and evidence-based practice for cutting-edge immunotherapies. Discusses clinical applications of immunotherapies in common types of cancer including melanoma, breast cancer, pancreatic cancer, colon and rectum cancer, prostate cancer, leukemias, and hematologic malignancies. Includes dedicated chapters on immune responses, innate immunity, active immunization against cancer, and cell-based therapies by leading experts. Covers key topics such as cancer immunotherapy in the presence of COVID-19, immunotherapeutics, new findings concerning the role of the microbiome in immunotherapy, advances in engineered cell-based therapies, and active immunization against cancer.

Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.