This unique volume presents AI in relation to ethical points of view in handling big data sets. Issues such as algorithmic biases, discrimination for specific patterns and privacy breaches may sometimes be skewed to affect research results so that certain fields to appear more appealing to funding agencies. The discussion on the ethics of AI is highly complex due to the involvement of many international stakeholders such as the UN, OECD, parliaments, industry groups, professional bodies, and individual companies. The issue of reliability is addressed including the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots and cyber security tools. Features Discusses artificial intelligence and ethics, the challenges and opportunities Presents the issue of reliability in the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots, and cyber security tools Ethical responsibility and reasoning for using AI in Big Data Addresses practicing medicine and ethical issues when applying artificial intelligence

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.

The healthcare industry is grappling with numerous challenges, including rising costs, inefficiencies in service delivery, and the need for personalized treatment approaches. Traditional healthcare management and delivery methods must be improved in addressing these issues, leading to a growing demand for innovative solutions. Additionally, the exponential growth of medical data and the complexity of biomedical research and biotechnology presents a daunting challenge in harnessing this data effectively for improved patient care and medical advancements. There is a pressing need for a comprehensive understanding of how artificial intelligence (AI) can be leveraged to tackle these challenges and drive meaningful change in the healthcare sector. Future of AI in Biomedicine and Biotechnology offers a timely and insightful solution to the challenges faced by the healthcare industry. This book is not just a theoretical exploration; it is a practical roadmap for healthcare professionals, researchers, policymakers, and entrepreneurs seeking to navigate the complexities of AI in healthcare. By exploring the intersection of AI with biomedical sciences and biotechnology, this book provides a comprehensive guide to harnessing the power of AI for transformative healthcare innovation.

This book covers a range of topics on exploiting Nigeria’s mega biodiversity for food security and health; DNA forensic science and its applications; medical biotechnology and biopharmaceutics; medicinal and underutilized plants; impact and mitigation of antibiotic resistance; bioinformatics applications; medical insect biotechnology; etc. The book will be useful reference material for the scientists and researchers working in the fields of nutraceuticals, molecular diagnostics and DNA forensics, biopharmaceuticals and medical biotechnology, nanotechnology, antimicrobials from indigenous plant species, bioinformatics, etc. Emphasizes recent advances in biotechnologies that will help in tackling emerging global health challenges Provides detailed information on how to harness indigenous bioresources including microorganisms and plants for healthcare delivery Introduces new frontiers in the areas of molecular diagnostics and DNA forensic science and bioinformatics with case studies, recent advances in medical insect biotechnology and molecular genetics of pest use towards the exploitation of arthropod midgut components to develop interventions against infectious diseases Reviews bioactive molecules derived from commonly used and underutilized medicinal plants that could be used to develop novel drugs for improved healthcare delivery Discusses current approaches in medical and biopharmaceutical biotechnology, deployment of inexpensive genomics-based vector surveillance for effective disease outbreak prediction and control of mosquito-borne viruses Hajiya Mairo Inuwa, Ph.D., is Professor in the Department of Biochemistry and Formerly Director, Centre for Biotechnology Research and Training (CBR&T), Ahmadu Bello University, Zaria, Nigeria. Ifeoma Maureen Ezeonu, Ph.D., is Professor of Medical Microbiology and Molecular Genetics in the Department of Microbiology, University of Nigeria, Nsukka, Nigeria. Charles Oluwaseun Adetunji, Ph.D., is Associate Professor of Microbiology and Biotechnology and Director of Intellectual Property and Technology Transfer, Edo State University, Uzairue, Nigeria. Abubakar Gidado, Ph.D., is Professor of Biochemistry and Director of North-East Zonal Biotechnology Centre of Excellence at the University of Maiduguri. Emmanuel Olufemi Ekundayo, Ph.D., is Associate Professor of Medical Microbiology and Microbial Genetics, Michael Okpara University of Agriculture, Umudike, Nigeria. Abdulrazak B. Ibrahim, Ph.D., is a Capacity Development Expert at the Forum for Agricultural Research in Africa (FARA) and Associate Professor of Biochemistry, Ahmadu Bello University, Zaria, Nigeria. Benjamin Ewa Ubi, Ph.D., is a Professor of Plant Breeding and Biotechnology and Director, Biotechnology Research and Development Centre, Ebonyi State University, Abakaliki, Nigeria.

Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. - Provides practical information on how to choose and use appropriate computational tools - Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format - Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics

This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.

This comprehensive anthology represents the key issues and problems in the field of medical ethics through the most up-to-date readings and case studies available. Each of the book's six parts is prefaced with helpful introductions that raise important questions and skillfully contextualize the positions and main points of the articles that follow.