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QSAR in Safety Evaluation and Risk Assessment
QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment. Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide profes...
Machine Learning and Deep Learning in Computational Toxicology
This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of ap...
Predicting Chemical Toxicity and Fate
Quantitative Structure-Activity Relationships (QSARs) are increasingly used to predict the harmful effects of chemicals to humans and the environment. The increased use of these methods in a variety of areas (academic, industrial, regulatory) results from a realization that very little toxicological or fate data is available on the vast amount of chemicals to which humans and the environment are exposed. Predicting Chemical Toxicity and Fate provides a comprehensive explanation of the state-of-the-art methods that are available to predict the effects of chemicals on humans and the environment. It describes the use of predictive methods to estimate the physiochemical properties, biological ac...
Advances in Computational Biology
Proceedings of The 2009 International Conference on Bioinformatics and Computational Biology in Las Vegas, NV, July 13-16, 2009. Recent advances in Computational Biology are covered through a variety of topics. Both inward research (core areas of computational biology and computer science) and outward research (multi-disciplinary, Inter-disciplinary, and applications) will be covered during the conferences. These include: Gene regulation, Gene expression databases, Gene pattern discovery and identification, Genetic network modeling and inference, Gene expression analysis, RNA and DNA structure and sequencing, Biomedical engineering, Microarrays, Molecular sequence and structure databases, Molecular dynamics and simulation, Molecular sequence classification, alignment and assembly, Image processing In medicine and biological sciences, Sequence analysis and alignment, Informatics and Statistics in Biopharmaceutical Research, Software tools for computational biology and bioinformatics, Comparative genomics; and more.
OMICS
A reflection of the explosion of research and development in this field, OMICS: Biomedical Perspectives and Applications explores applications of omics in bioinformatics, cancer research and therapy, diabetes research, plant science, molecular biology, and neurosciences. A select editorial panel of experts discusses their cutting edge omics researc
Drug Repositioning: Current Advances and Future Perspectives
Drug repositioning is the process of identifying new indications for existing drugs. At present, the conventional de novo drug discovery process requires an average of about 14 years and US$2.5 billion to approve and launch a drug. Drug repositioning can reduce the time and cost of this process because it takes advantage of drugs already in clinical use for other indications or drugs that have cleared phase I safety trials but have failed to show efficacy in the intended diseases. Historically, drug repositioning has been realized through serendipitous clinical observations or improved understanding of disease mechanisms. However, recent technological advances have enabled a more systematic approach to drug repositioning. This eBook collects 16 articles from 112 authors, providing readers with current advances and future perspectives of drug repositioning.
AACR 2017 Proceedings: Abstracts 1-3062
The AACR Annual Meeting highlights the best cancer science and medicine from institutions all over the world. Attendees are invited to stretch their boundaries, form collaborations, attend sessions outside their own areas of expertise, and learn how to apply exciting new concepts, tools, and techniques to their own research. Part A contains abstracts 1-3062 accepted for the 2017 meeting.
Batch Effects and Noise in Microarray Experiments
Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise...
Multifunctional Inorganic Nanomaterials for Energy Applications
Multifunctional Inorganic Nanomaterials for Energy Applications provides deep insight into the role of multifunctional nanomaterials in the field of energy and power generation applications. It mainly focuses on the synthesis, fabrication, design, development, and optimization of novel functional inorganic nanomaterials for energy storage and saving devices. It also covers studies of inorganic electrode materials for supercapacitors, membranes for batteries and fuel cells, and materials for display systems and energy generation. Features: Explores computational and experimental methods of preparing inorganic nanomaterials and their multifunctional applications Includes synthesis and performance analysis of various functional nanomaterials for energy storage and saving applications Reviews current research directions and latest developments in the field of energy materials Discusses importance of computational techniques in designing novel nanomaterials Highlights importance of multifunctional applications of nanomaterials in the energy sector This book is aimed at graduate students and researchers in materials science, electrical engineering, and nanomaterials.
