In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.

As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.

The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level. Dealing with artificial intelligence, this book delineates AI’s role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science. Key Features: Introduces new and advanced methods of model discovery for time series data using artificial intelligence Implements topological approaches to distill "machine-intuitive" models from complex dynamics data Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations Heralds a new era in data-driven science and engineering based on the operational concept of "computational intuition" Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects.

This book explores the possibility of the use of artificial intelligence (AI) to solve one of the cosmos’ biggest mysteries: the nature of undetectable forms of matter, namely dark matter and dark energy, which make up 95% of the universe. The book describes the outcome of this quest in terms of an entangled ur-universe that admits no observer, and incorporates an extra dimension to encode space-time as a latent manifold. A cosmic engine fueled by dark energy that maintains the topology of the universe during its expansion, involving autocatalytic vacuum creation, is identified. The physical picture of the cosmos presented in the book paves the way for a solution to the cosmological constant problem and provides a cogent explanation for the huge gap between the predicted and measured values that has troubled physicists for decades.

The dark universe contains matter and energy unidentifiable with current physical models, accounting for 95% of all the matter and energetic equivalent in the universe. The enormous surplus brings up daunting enigmas, such as the cosmological constant problem and the apparent distortions in the dynamics of deep space, and so coming to grips with the invisible universe has become a scientific imperative. This book addresses this need, reckoning that no cogent physical model of the dark universe can be implemented without first addressing the metaphysical hurdles along the way. The foremost problem is identifying the topology of the universe which, as argued in the book, is highly relevant to unveil the secrets of the dark universe. Artificial Intelligence (AI) is a valuable tool in this effort since it can reconcile conflicting data from deep space with the extant laws of physics by building models to decipher the dark universe. This book explores the applications of AI and how it can be used to embark on a metaphysical quest to identify the topology of the universe as a prerequisite to implement a physical model of the dark sector that enables a meaningful extrapolation into the visibile sector. The book is intended for a broad readership, but a background in college-level physics and computer science is essential. The book will be a valuable guide for graduate students as well as researchers in physics, astrophysics, and computer science focusing on AI applications to elucidate the nature of the dark universe. Key Features: · Provides readers with an intellectual toolbox to understand physical arguments on dark matter and energy. · Up to date with the latest cutting-edge research. · Authored by an expert on artificial intelligence and mathematical physics.

Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field. Artificial Intelligence for Medicine is beneficial for academics, professionals in the IT industry, educators, students, and anyone else involved in the use and development of AI in the medical field. Covers the basic concepts of Artificial Intelligence and Machine Learning, methods and practices, and advanced topics and applications to clinical and precision medicine Presents readers with an understanding of how AI is revolutionizing medicine by demonstrating the applications of computational intelligence to the field, along with an awareness of how AI can improve upon traditional medical structures Provides researchers, practitioners, and project stakeholders with a complete guide for applying AI techniques in their projects and solutions

Immunotherapy with genetically engineered immune cell products is a transformative treatment modality with potential applications in various fields of medicine. A prime example is chimeric antigen receptor (CAR)-modified T cells in hematology and oncology, and the advent of CAR T cell therapies to treat infectious diseases, autoimmune disorders, and cardiovascular diseases. The medical need and demand from patients and caregivers require radical innovations to accelerate and improve pre-clinical development and clinical translation, provision of gene-transfer vectors, and immune cell product manufacturing as well as a critical reflection and discussion on ethical and socioeconomic aspects. The goal of this special issue of Frontiers in Immunology is to provide a comprehensive and multi-faceted view on the current state-of-the-art, imminent and future directions the field is taking in order to accelerate the pre-clinical development, clinical translation, and manufacturing of CAR T cells, increase access and sustainability of CAR T cell therapy for health care systems (in developed and in developing countries). This special issue will focus on the medical and scientific dimension incl. approved and emerging indications, new areas in medicine, advanced gene-transfer and gene-editing technologies, innovations in pre-clinical assessment (efficacy, toxicology, genomic safety), innovations in scalable automated manufacturing (bioprocessing), the implementation of high content data acquisition, machine learning and artificial intelligence, innovations in clinical trial design; and consider the ethical, socioeconomic and societal dimension of CAR T cells in particular and gene-engineered immune cell therapy in general.

Translational Bioinformatics in Healthcare and Medicine offers an overview of main principles of bioinformatics, biological databases, clinical informatics, health informatics, viroinformatics and real-case applications of translational bioinformatics in healthcare. Written by experts from both technology and clinical sides, the content brings together essential knowledge to make the best of recent advancements of the field. The book discusses topics such as next generation sequence analysis, genomics in clinical care, IoT applications, blockchain technology, patient centered interoperability of EHR, health data mining, and translational bioinformatics methods for drug discovery and drug repurposing. In addition, it discusses the role of bioinformatics in cancer research and viroinformatics approaches to counter viral diseases through informatics. This is a valuable resource for bioinformaticians, clinicians, healthcare professionals, graduate students and several members of biomedical field who are interested in learning more about how bioinformatics can impact in their research and practice. - Covers recent advancements in translational bioinformatics and its healthcare applications - Discusses integrative and multidisciplinary approaches to U-healthcare systems development and management - Bridges the gap among various knowledge domains in the field, integrating both technological and clinical knowledge into practical content

In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials.