تحسين النتائح
ترتيب حسب
من
الى
الصفحة 2
الصفحة 2
Biomedical Informatics : Computer Applications in Health Care and Biomedicine
This series is directed to healthcare professionals who are leading the transformation of health care by using information and knowledge. Launched in 1988 as Computers in Health Care, the series offers a broad range of titles: some addressed to specific prof- sions such as nursing, medicine, and health administration; others to special areas of practice such as trauma and radiology.
Bioinformatics tools for pharmaceutical drug product development
Presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. And include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall.
Bioinformatics of genome regulation and structure II
The conference was organized by the Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia. The material covers the most recent topics in bioinformatics, including (i) regulatory genomic sequences: databases, knowledge bases, computer analysis, modeling, and recognition; (ii) large-scale genome analysis and functional annotation; (iii) gene structure detection and prediction; (iv) comparative and evolutionary genomics; (v) computer analysis of genome polymorphism and evolution; computer analysis and modeling of transcription, splicing, and translation; structural computational biology: structure- function organization of genomic DNA, RNA, and proteins; (vi) gene networks, signal transduction pathways, and genetically controlled metabolic pathways: databases, knowledge bases, computer analysis, and modeling; principles of organization, operation, and evolution; (vii) data warehousing, knowledge discovery and data mining; and (viii) analysis of basic patterns of genome operation, organization, and evolution.
Bioinformatics drug discovery
Quantitative tools are becoming increasingly important in order to understand complex cascade of signal transduction events, pathways or biochemical reactions. The book showcases how computational techniques and algorithms are applied to biological data analysis, interpretation, and modelling. It covers applications in drug design and discovery, immune systems, phylogenetic analysis and protein structures.
Bioinformatics and the cell : Modern computational approaches in genomics, proteomics and transcriptomics
"Xuhua Xia’s Bioinformatics and the Cell is a welcome addition to the bourgeoning field of bioinformatics text books. Xia stakes out a too-often neglected middle ground in bioinformatics by presenting a work that emphasizes methods’ biological utility without eschewing algorithmic formalism. Readers will find a well-rounded presentation of bioinformatics techniques employed in genomics, transcriptomics and proteomics – unified throughout by the common theme of molecular evolution."
Bioinformatics and systems biology : Collaborative research and resources
Collaborative research in bioinformatics and systems biology is a key element of modern biology and health research. This book highlights and provides access to many of the methods, environments, results and resources involved, including integral laboratory data generation and experimentation and clinical activities. Collaborative projects embody a research paradigm that connects many of the top scientists, institutions, their resources and research worldwide, resulting in first-class contributions to bioinformatics and systems biology. Central themes include describing processes and results in collaborative research projects using computational biology and providing a guide for researchers to access them. The book is also a practical guide on how science is managed. It shows how collaborative researchers are putting results together in a way accessible to the entire biomedical community.
Bioimage Data Analysis Workflows
This book provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python.
Bacterial Genomes and Infectious Diseases
This book imparts fundamental knowledge on the structure, organization, and evolution of bacterial genomes. The value and power of comparative genomics and proteomics, bioinformatics, microarrays, and knockout animal models in analyzing genomes, bacteria-host interactions and disease are demonstrated. Also discussed are the genomes of virulent and nonvirulent strains and species, origin and evolution of pathogens, different models of bacteria-host interactions, and diseases mechanisms.
Autoformation et aide au diagnostic en hématologie avec le logiciel ADH = Self-study and diagnostic aid in hematology with ADH software
The software includes a rich iconography on blood and marrow cells which will complement the knowledge in cytology of hematologists. Certain cytological abnormalities encountered in certain pathologies are also presented.
Artificial intelligence in drug design
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.
Applied Bioinformatics : An Introduction
In this book, anyone who can operate a PC, standard software and the Internet will learn to understand the biological basis of bioinformatics of the existence as well as the source and availability of bioinformatics software how to apply these tools and interpret results with confidence.This is aided by introductory chapters to important aspects of bioinformatics, detailed bioinformatics exercises, including solutions and a glossary of definitions and terminology relating to bioinformatics.
An introduction to generative drug discovery
Describes the state‑of‑the‑art methods and applications for de novo design of drug candidates using generative chemistry models as well as the ethical aspects of this technology. It will provide a foundation for those new to the field as well as those that may already have some experience of its utility. With contributions from scientists in both academia and industry ‘Introduction to Generative Drug Discovery’ may represent one of the earliest if not the first book to focus on this topic.
AI in clinical medicine : A practical guide for healthcare professionals
A Practical Guide for Healthcare Professionals is the definitive reference book for the emerging and exciting use of AI throughout clinical medicine. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals is divided into four sections. Section 1 provides readers with the basic vocabulary that they require, a framework for AI, and highlights the importance of robust AI training for physicians. Section 2 reviews foundational ideas and concepts, including the history of AI. Section 3 explores how AI is applied to specific disciplines. Section 4 describes emerging trends, and applications of AI in medicine in the future.
African Biodiversity : molecules, organisms, ecosystems
BIOTA is an interdisciplinary research project focusing on sustainable use and conservation of biodiversity in Africa (http://www.biote-africa.de). Session titles were Biogeography and Speciation Processes, Phylogenetic Patterns and Systematics, Diversity Declines and Conservation, and Applied Biodiversity Informatics
Advances in cognitive neurodynamics ICCN 2007; Proceedings of the International conference on cognitive neurodynamics - 2007
Contains the Proceedings of the 1st International Conference on Cognitive Neurodynamics held in Shanghai, November 17-21, 2007. The participants were treated to an exciting and stimulating conference that left everyone with an enthusiastic vision for the future of the field. The latest important progress was covered by 13 mini-symposia including: Models of Mental Disorders; Cognitive Machines; Dynamics in learning and memory; Central nervous system synchronization; Neuroinformatics; Cognitive Computational Modeling of Human Language Processing; Cognitive Neurodynamics of Attention; Bottom-Up and Top-Down; Brain Networks; From Anatomy to Dynamics; Translational Cognitive Neuroimaging; K-sets; Theory and Applications; Advanced Signal Processing Techniques for Brain Data Analysis; Visual cortex: information processing and dynamics; Dynamics of Firing Patterns and Synchronization in Neuronal Systems.
Advanced soft electronics in biomedical engineering : materials, manufactures, and applications
Presents the latest advances in soft electronics in biomedical engineering and its potential applications in various biomedical fields. Provide comprehensive coverage of how soft electronics are used in diagnostics and monitoring, medical therapy, neural engineering, and wearable and implantable systems. In particular, some emerging research areas such as advanced soft robotics, fiber sensing technologies, and power optimization strategies are explored. In addition, the book highlights international standardization activities in wearable technologies and implantable bioelectronics.
Leveraging Data Science for Global Health
Explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources.
Machine learning in healthcare : Fundamentals and recent applications
Discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
