Ethical Issues In Ai For Bioinformatics And Chemoinformatics
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Ethical Issues in AI for Bioinformatics and Chemoinformatics by Yashwant V. Pathak,Surovi Saikia,Sarvadaman Pathak,Jayvadan K. Patel,Jigna Bhupendra Prajapati Pdf
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
Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods by Lilhore, Umesh Kumar,Kumar, Abhishek,Simaiya, Sarita,Vyas, Narayan,Dutt, Vishal Pdf
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.
Artificial Intelligence in Bioinformatics and Chemoinformatics by Yashwant Pathak,Surovi Saikia,Sarvadaman Pathak,Jayvadankumar Patel,Bhupendra Gopalbhai Prajapati Pdf
The authors aim to shed light on the practicality of using machine learning in finding complex chemoinformatics and bioinformatics applications as well as identifiying AI in biological and chemical data points. The chapters are designed in such a way that they highlight the important role of AI in chemistry and bioinformatics particularly for the classification of diseases, selection of features and compounds, dimensionality reduction and more. In addition, they assist in the organization and optimal use of data points generated from experiments performed using AI techniques. This volume discusses the development of automated tools and techniques to aid in research plans. Features Covers AI applications in bioinformatics and chemoinformatics Demystifies the involvement of AI in generating biological and chemical data Provides an Introduction to basic and advanced chemoinformatics computational tools Presents a chemical biology based toolset for artificial intelligence usage in drug design Discusses computational methods in cancer, genome mapping, and stem cell research
Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences by Navneet Sharma,Himanshu Ojha,Pawan Raghav,Ramesh K. Goyal Pdf
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
Medical Biotechnology, Biopharmaceutics, Forensic Science and Bioinformatics by Hajiya Mairo Inuwa,Ifeoma Maureen Ezeonu,Charles Oluwaseun Adetunji,Emmanuel Olufemi Ekundayo,Abubakar Gidado,Abdulrazak B. Ibrahim,Benjamin Ewa Ubi Pdf
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.
Artificial Intelligence in Drug Design by Alexander Heifetz Pdf
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.
Artificial Intelligence in Drug Discovery by Nathan Brown Pdf
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.
Practical Chemoinformatics by Muthukumarasamy Karthikeyan,Renu Vyas Pdf
Chemoinformatics is equipped to impact our life in a big way mainly in the fields of chemical, medical and material sciences. This book is a product of several years of experience and passion for the subject written in a simple lucid style to attract the interest of the student community who wish to master chemoinformatics as a career. The topics chosen cover the entire spectrum of chemoinformatics activities (methods, data and tools). The algorithms, open source databases, tutorials supporting theory using standard datasets, guidelines, questions and do it yourself exercises will make it valuable to the academic research community. At the same time every chapter devotes a section on development of new software tools relevant for the growing pharmaceutical, fine chemicals and life sciences industry. The book is intended to assist beginners to hone their skills and also constitute an interesting reading for the experts.
Proceedings of the International Conference on Big Data, IoT, and Machine Learning by Mohammad Shamsul Arefin,M. Shamim Kaiser,Anirban Bandyopadhyay,Md. Atiqur Rahman Ahad,Kanad Ray Pdf
This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.
Helps you choose the right computational tools and techniques to meet your drug design goals Computational Drug Design covers all of the major computational drug design techniques in use today, focusing on the process that pharmaceutical chemists employ to design a new drug molecule. The discussions of which computational tools to use and when and how to use them are all based on typical pharmaceutical industry drug design processes. Following an introduction, the book is divided into three parts: Part One, The Drug Design Process, sets forth a variety of design processes suitable for a number of different drug development scenarios and drug targets. The author demonstrates how computational techniques are typically used during the design process, helping readers choose the best computational tools to meet their goals. Part Two, Computational Tools and Techniques, offers a series of chapters, each one dedicated to a single computational technique. Readers discover the strengths and weaknesses of each technique. Moreover, the book tabulates comparative accuracy studies, giving readers an unbiased comparison of all the available techniques. Part Three, Related Topics, addresses new, emerging, and complementary technologies, including bioinformatics, simulations at the cellular and organ level, synthesis route prediction, proteomics, and prodrug approaches. The book's accompanying CD-ROM, a special feature, offers graphics of the molecular structures and dynamic reactions discussed in the book as well as demos from computational drug design software companies. Computational Drug Design is ideal for both students and professionals in drug design, helping them choose and take full advantage of the best computational tools available. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Markov Decision Processes in Artificial Intelligence by Olivier Sigaud,Olivier Buffet Pdf
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.
Machine Learning in Chemistry by Hugh M Cartwright Pdf
Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.
Applications of Machine Learning by Prashant Johri,Jitendra Kumar Verma,Sudip Paul Pdf
This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.
Chemometrics and Cheminformatics in Aquatic Toxicology by Kunal Roy Pdf
CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY Explore chemometric and cheminformatic techniques and tools in aquatic toxicology Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms. You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods. Readers will also benefit from the inclusion of: A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data mining An exploration of aquatic toxicity databases, chemometric software tools, and webservers Practical examples and case studies to highlight and illustrate the concepts contained within the book A concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity data Perfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.
