Learn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolution An outline of the fundamental breakthroughs in AI and deep learning and their applications across medicine A compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeutics A summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic development A guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government.

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.

"COVID-19 and Omics Technologies" is a comprehensive, integrative assessment of recent information and knowledge collected on SARS-CoV-2 and COVID-19 during the pandemic based on omics technologies. It demonstrates how omics technologies could better investigate the infectious disease and propose solutions to the current concerns. The value of multi-omics technologies in understanding disease etiology and host response, discovering infection biomarkers and illness prediction, identifying vaccine candidates, discovering therapeutic targets, and tracing pathogen evolution is discussed in this book. These factors combine to make it a valuable resource to enhance understanding of both "Omics technology" and "COVID-19" as a disease. The book covers the most recent understanding of COVID-19 and the applications of cutting-edge studies, making it accessible to a large multidisciplinary readership. The book explains how high-throughput technologies and systems biology might assist to solve the pandemic’s challenges and deconstruct and appreciate the substantial contributions that omics technologies have made in predicting the path of this unforeseeable pandemic. Features: In-depth summary of clinical presentation, epidemiological impact, and long-term sequelae of COVID-19 pandemic. A systematic overview of omics-based approaches to the study of COVID-19 biology. Recent research results and some pointers to future advancements in methodologies used. Detailed examples from recent studies on COVID-19 encompassing different omics methodologies. A detailed description of methodologies and notes on the applications of state-of-the-art technologies. This book is intended for scientists who need to understand the biology of COVID-19 from the perspective of omics investigations, as well as researchers who want to employ omics-based technologies in disease biology.

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise