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Chemical pollution --- Contamination of environment --- Environment and state --- Environmental control --- Environmental effects --- Environmental management --- Environmental management (Government policy) --- Environmental policy --- Environmental pollution --- Environmental stresses --- Environnement [Politique de l'] --- Environnement--Gestion --- Gestion de l'environnement --- Milieubeheer --- Milieubeleid --- Politique de l'environnement --- Pollution --- Pollution--Control --- Pollution--Prevention --- State and environment --- Verontreiniging --- Vervuiling --- Genetic engineering. --- Genetic engineering --- Moral and ethical aspects. --- Human Genome Project. --- Human Genome Project --- BIOMEDICAL SCIENCES/Evolution --- BIOMEDICAL SCIENCES/General --- Designed genetic change --- Engineering, Genetic --- Gene splicing --- Genetic intervention --- Genetic surgery --- Genetic recombination --- Biotechnology --- Transgenic organisms --- HGP --- H.G.P. --- General ethics --- Human genetics --- Moral and ethical aspects --- Genetic engineering - Moral and ethical aspects.

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An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

Stochastic processes --- Molecular biology --- Artificial intelligence. Robotics. Simulation. Graphics --- Bioinformatics --- Neural networks (Computer science) --- Machine learning --- Markov processes --- Bio-informatique --- Biologie moléculaire --- Réseaux neuronaux (Informatique) --- Apprentissage automatique --- Markov, Processus de --- Computer simulation --- Mathematical models --- Modèles mathématiques --- Bioinformatics. --- Machine learning. --- Markov processes. --- Computer simulation. --- Mathematical models. --- Neural networks (Computer science). --- Biologie moléculaire --- Réseaux neuronaux (Informatique) --- Modèles mathématiques --- Molecular biology - Computer simulation. --- Molecular biology - Mathematical models.

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TOC:http://www.loc.gov/catdir/toc/cam025/2001052862.html

Gene expression --- DNA microarrays --- DNA microarrays. --- Gene expression. --- Molecular biology --- Mathematical statistics --- Biologie --- Data-analyse --- DNA --- Genetica --- Genes --- Genetic regulation --- DNA biochips --- Microarrays, DNA --- Biochips --- Immobilized nucleic acids --- Expression --- Monograph

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Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.  Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.  Takes a modern approach based on mathematical, probabilistic, and graphical modeling.  Provides an integrated presentation of theory, examples, exercises and applications.  Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences. "This book is fascinating!" - David Hand (Imperial College, UK) "This book provides an extremely useful introduction to the intellectually stimulating problems of data mining electronic business." - Andreas S. Weigend (Chief Scientist, Amazon.com)

Stochastic processes --- Internet --- Telecommunication --- World Wide Web --- Cyberspace --- Probabilities. --- Télécommunications --- Probabilités --- Mathematical models. --- Traffic --- Trafic --- Modèles mathématiques --- Probabilities --- Mathematical models --- Web --- Cyberespace --- Télécommunications --- Probabilités --- Modèles mathématiques --- Internet - Mathematical models --- Telecommunication - Traffic - Mathematical models --- World Wide Web - Mathematical models --- Cyberspace - Mathematical models