This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

"The development and application of bioinformatics tools to basic and translational cancer research is, in fact, a rapidly expanding field that deserves a timely review. Therefore, a publication of this type is needed. The editors have done an excellent job in recruiting well-established scientists to author the various chapters of the book." —Dieter Naf, Jackson Laboratory, USA Cancer bioinformatics is now emerging as a new interdisciplinary field, which is facilitating an unprecedented synthesis of knowledge arising from the life and clinical sciences. This groundbreaking title provides a comprehensive and up-to-date account of the enormous range of bioinformatics for cancer therapy development from the laboratory to clinical trials. It functions as a guide to integrated data exploitation and synergistic knowledge discovery, and support the consolidation of the interdisciplinary research community involved.

Many chemotherapeutic agents are available in today’s market that are highly effective against a variety of cancer types; however, the major drawbacks of these chemotherapeutic agents are the many side effects. As an alternative to these chemotherapeutic agents, there are a number of natural agents that are effective against cancer that have been tested in preclinical and clinical models over the years. These natural products must be documented and discussed in order to provide a thorough overview of all the options available for cancer treatment. The Handbook of Research on Natural Products and Their Bioactive Compounds as Cancer Therapeutics emphasizes the list of natural agents against all types of cancers and discusses the current state of research in the fields of natural products and their derivatives against cancer in preclinical and clinical models. This book also provides insight into the applications of meditation and mindfulness-based interventions in clinical and non-clinical conditions. Covering topics such as cancer therapy, antioxidants, and flavonoids, it is ideal for students, research scholars, academicians, professors, scientists, oncologists, doctors, and medical practitioners.

This book presents current advances in the emerging interdisciplinary field of microRNA research of human cancers from a unique perspective of quantitative sciences: bioinformatics, computational and systems biology, and mathematical modeling. This volume contains adaptations and critical reviews of recent state-of-the-art studies, ranging from technological advances in microRNA detection and profiling, clinically oriented microRNA profiling in several human cancers, to a systems biology analysis of global patterns of microRNA regulation of signaling and metabolic pathways. Interactions with transcription factor regulatory networks and mathematical modeling of microRNA regulation are also discussed.

The book shows how mathematical and computational models can be used to study cancer biology. It introduces the concept of mathematical modeling and then applies it to a variety of topics in cancer biology. These include aspects of cancer initiation and progression, such as the somatic evolution of cells, genetic instability, and angiogenesis. The book also discusses the use of mathematical models for the analysis of therapeutic approaches such as chemotherapy, immunotherapy, and the use of oncolytic viruses.

Omics is an emerging and exciting area in the field of science and medicine. Numerous promising developments have been elucidated using omics (including genomics, transcriptomics, epigenomics, proteomics, metabolomics, interactomics, cytomics and bioinformatics) in cancer research. The development of high-throughput technologies that permit the solution of deciphering cancer from higher dimensionality will provide a knowledge base which changes the face of cancer understanding and therapeutics. This is the first book to provide such a comprehensive coverage of a rapidly evolving area written by leading experts in the field of omics. It complies and details cutting-edge cancer research that covers the broad advances in the field and its application from cancer-associated gene discovery to drug target validation. It also highlights the potential of using integration approach for cancer research. This unique and timely book provides a thorough overview of developing omics, which will appeal to anyone involved in cancer research. It will be a useful reference book for graduate students of different subjects (medicine, biology, engineering, etc) and senior scientists interested in the fascinating area of advanced technologies in cancer research. Readership: This is a precious book for all types of readers – cancer researchers, oncologists, pathologists, biologists, clinical chemists, pharmacologists, pharmaceutical specialists, biostatisticians, and bioinformaticists who want to expand their knowledge in cancer research.