NEURAL NETWORKS AND GENOME INFORMATICS METHODS IN COMPUTATIONAL BIOLOGY AND BIOCHEMISTRY
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This book is a comprehensive reference in the field of neural networks and genome informatics. The tutorial of neural network foundations introduces basic neural network technology and terminology. This is followed by an in-depth discussion of special system designs for building neural networks for genome informatics, and broad reviews and evaluations of current state-of-the-art methods in the field. This book concludes with a description of open research problems and future research directions.
This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.
In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features § Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting § Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.
Author : A.K. Konopka
ISBN : 9781420015126
Genre : Science
File Size : 57.57 MB
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With extraordinary clarity,the Systems Biology: Principles, Methods, and Concepts focuses on the technical practical aspects of modeling complex or organic general systems. It also provides in-depth coverage of modeling biochemical, thermodynamic, engineering, and ecological systems. Among other methods and concepts based in logic, computer science, and dynamical systems, it explores pragmatic techniques of General Systems Theory. This text presents biology as an autonomous science from the perspective of fundamental modeling techniques. A complete resource for anyone interested in biology as an exact science, it includes a comprehensive survey, review, and critique of concepts and methods in Systems Biology.
Author : Nature Publishing Group
ISBN : 0333803868
Genre : Medical
File Size : 26.19 MB
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Nature Encyclopedia of the Human Genome is the only reference resource devoted entirely to the scientific basis and genetics and genomics research and the complex commercial, philosophical, and ethical questions that arise from it. It presents a comprehensive and rigorously detailed overview of current genome science and its groundbreaking applications, examining the many topics that surround the field from the differing perspectives of history, philosophy, ethics, law, medicine, public health, and industry. Core areas covered include: structural genomics, functional genomics, chromosome structure and function, evolution and comparative genomics, genome mapping and sequencing, genes and disease, behavioural and psychiatric genetics, mathematical and population genetics, proteomics, bioinformatics, ethical, legal and social issues and biographies or key figures.
Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.
This volume contains 18 peer-reviewed papers based on the presentations at the 10th Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2010) held at Kyoto University from July 26 to July 28, 2010. This workshop started in 2001 as an event for doctoral students and young researchers to present and discuss their research results and approaches in bioinformatics and systems biology. It is part of a collaborative educational program involving leading institutions and leaders committed to the following programs: bull; Boston - Graduate Program in Bioinformatics, Boston University bull; Berlin - The International Research Training Group (IRTG) "Genomics and Systems Biology of Molecular Networks" bull; Kyoto - The JSPS International Training Program (ITP) "International Research and Training Program of Bioinformatics and Systems Biology" bull; Tokyo - Global COE Program "Center of Education and Research for Advanced Genome-Based Medicine"
Author : Christian Lemaitre
ISBN : UOM:39015058889026
Genre : Artificial intelligence
File Size : 49.43 MB
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This book constitutes the refereed proceedings of the 9th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2004, held in Puebla, Mexico in November 2004. The 97 revised full papers presented were carefully reviewed and selected from 304 submissions. The papers are organized in topical sections on distributed AI and multi-agent systems, knowledge engineering and case-based reasoning, planning and scheduling, machine learning and knowledge acquisition, natural language processing, knowledge representation and reasoning, knowledge discovery and data mining, robotics, computer vision, uncertainty and fuzzy systems, genetic algorithms and neural networks, AI in education, and miscellaneous topics.