Artificial Intelligence And Biological Sciences
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Artificial Intelligence And Biological Sciences
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Author : P. V. Mohanan (Toxicologist)
language : en
Publisher:
Release Date : 2025
Artificial Intelligence And Biological Sciences written by P. V. Mohanan (Toxicologist) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Science categories.
"Artificial intelligence (AI) can be generally defined as the ability or capacity of machines that can simulate the intelligence capability of higher organisms and the term was first coined by John McCarthy in 1956. AI has confirmed their root in many branches of research especially in mathematics, physiology, computing, biology and psychology. Ideally, an AI system should response logically, self-aware and the ability to learn from experience and be discern and respond according to the external environments. With deep learning and machine learning algorithm-based intellect systems that can perform the activities requiring human intellect can be developed"--
Artificial Intelligence And Biological Sciences
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Author : P.V. Mohanan
language : en
Publisher: CRC Press
Release Date : 2025-06-17
Artificial Intelligence And Biological Sciences written by P.V. Mohanan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
Advancements of AI in medical and biological sciences have opened new ways for drug development. Novel therapeutic molecules and their target action can be easily predicted and can be modified. AI helps in disease detection and diagnosis faster. The breakthrough of AI is made especially in the area of personalized precision medicine, host-pathogen interaction and predictive epidemiology. These approaches could help in faster decision-making with minimal errors that can improve risk analysis, especially disease diagnosis and selecting treatment strategy. In agricultural practices, an exact combination of fertilizers, pesticides, herbicides, soil management, water requirement analysis, yield prediction and overall crop management can be modified by implementing AI interventions. AI could provide a better improvement in agriculture, medical research, pharmaceuticals and bio-based industries for a sustainable life. The key features of this book are: AI in medical Sciences, biotechnology and drug discovery; Application of AI in Digital Pathology, cytology and bioinformatics; Overview of AI, Machine Learning and Deep Learning; Impact of Artificial Intelligence in Society; Artificial Intelligence in Pharmacovigilance; and Ethics in Artificial Intelligence. The volume aims to comprehensively cover the application of AI in biological sciences. It is a collection of contributions from different authors who have several years of experience in their specific areas. The book will be useful for pharma companies, CROs, product developers, students, researchers, academicians, policymakers and practitioners.
Artificial Intelligence And Molecular Biology
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Author : Lawrence Hunter
language : en
Publisher:
Release Date : 1993
Artificial Intelligence And Molecular Biology written by Lawrence Hunter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.
These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.
Machine Learning In Biological Sciences
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Author : Shyamasree Ghosh
language : en
Publisher: Springer Nature
Release Date : 2022-05-04
Machine Learning In Biological Sciences written by Shyamasree Ghosh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-04 with Medical categories.
This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.
Biomedical Data Mining For Information Retrieval
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Author : Sujata Dash
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-24
Biomedical Data Mining For Information Retrieval written by Sujata Dash and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-24 with Computers categories.
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.
Intelligence Science
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Author : Zhongzhi Shi
language : en
Publisher: Elsevier
Release Date : 2021-04-16
Intelligence Science written by Zhongzhi Shi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-16 with Computers categories.
Intelligence Science: Leading the Age of Intelligence covers the emerging scientific research on the theory and technology of intelligence, bringing together disciplines such as neuroscience, cognitive science, and artificial intelligence to study the nature of intelligence, the functional simulation of intelligent behavior, and the development of new intelligent technologies. The book presents this complex, interdisciplinary area of study in an accessible volume, introducing foundational concepts and methods, and presenting the latest trends and developments. Chapters cover the Foundations of neurophysiology, Neural computing, Mind models, Perceptual intelligence, Language cognition, Learning, Memory, Thought, Intellectual development and cognitive structure, Emotion and affect, and more. This volume synthesizes a very rich and complex area of research, with an aim of stimulating new lines of enquiry. - Presents a complex, interdisciplinary area in an accessible way, including the latest trends and developments - Brings together disciplines such as neuroscience, cognitive science and artificial intelligence - Gives the latest methods and theories in the development of new intelligent technologies - Reflects upon the most important achievements in the study of natural and artificial intelligence - Contextualizes intelligence research within the history and progress of twenty-first century science
Artificial Neural Networks In Biological And Environmental Analysis
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Author : Grady Hanrahan
language : en
Publisher: CRC Press
Release Date : 2011-01-18
Artificial Neural Networks In Biological And Environmental Analysis written by Grady Hanrahan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-18 with Mathematics categories.
Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound
A Biologist S Guide To Artificial Intelligence
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Author : Ambreen Hamadani
language : en
Publisher: Elsevier
Release Date : 2024-02-29
A Biologist S Guide To Artificial Intelligence written by Ambreen Hamadani and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Science categories.
A Biologist's Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist's perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future.This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. - Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning - Equips with new data mining strategies an easy interface into the world of Artificial Intelligence - Enables researchers to enhance their own sphere of researching Artificial Intelligence
Contemporary Studies In Biological Sciences I
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Author : Semra BENZER
language : en
Publisher: Akademisyen Kitabevi
Release Date : 2023-12-31
Contemporary Studies In Biological Sciences I written by Semra BENZER and has been published by Akademisyen Kitabevi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-31 with Nature categories.
Deep Learning For The Life Sciences
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Author : Bharath Ramsundar
language : en
Publisher: O'Reilly Media
Release Date : 2019-04-10
Deep Learning For The Life Sciences written by Bharath Ramsundar and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Science categories.
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working