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Machine Learning Thomas Dietterich, Editor  \nChristopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate Editors  \nBioinformatics: The Machine Learning Approach, Pierre Baldi and Søren Brunak Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto  \nPierre Baldi Søren Brunak  \nBioinformatics  \nThe Machine Learning Approach  \nA Bradford Book The MIT Press Cambridge, Massachusetts London, England  \n􀀁c 2001 Massachusetts Institute of Technology  \nAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.  \nThis book was set in Lucida by the authors and was printed and bound in the United States of America.  \nLibrary of Congress Cataloging-in-Publication Data  \nBaldi, Pierre.  \nBioinformatics : the machine learning approach / Pierre Baldi, Søren Brunak.—2nd ed.  \n[p. cm](p. cm).—(Adaptive computation and machine learning)\"A Bradford Book\"  \nIncludes bibliographical references (p. ) .  \nISBN 0-262-02506-X (hc. : alk. paper)  \n1. Bioinformatics. 2. Molecular biology—Computer simulation. 3. Molecular biology—Mathematical models. 4. Neural networks (Computer science) . 5. Machine learning. 6. Markov processes. I. Brunak, Søren. II. Title. III. Series. QH506.B35 2001  \n572.8􀀂 01􀀂 13—dc21  \n2001030210  \nSeries Foreword  \nThe first book in the new series on Adaptive Computation and Machine Learning, Pierre Baldi and Søren Brunak’s Bioinformatics provides a comprehensive introduction to the application of machine learning in bioinformatics. The development of techniques for sequencing entire genomes is providing astronomical amounts of DNA and protein sequence data that have the potential to revolutionize biology. To analyze this data, new computational tools are needed—tools that apply machine learning algorithms to fit complex stochastic models. Baldi and Brunak provide a clear and unified treatment of statistical and neural network models for biological sequence data. Students and researchers in the fields of biology and computer science will find this a valuable and accessible introduction to these powerful new computational techniques.  \nThe goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. Recently, several research communities have begun to converge on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. The MIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high quality research and innovative applications.  \nThomas Dietterich  \nContents  \nSeries Foreword ix  \nPreface xi  \n1 Introduction 1  \n1.1 Biological Data in Digital Symbol Sequences 1  \n1.2 Genomes—Diversity, Size, and Structure 7  \n1.3 Proteins and Proteomes 16  \n1.4 On the Information Content of Biological Sequences 24  \n1.5 Prediction of Molecular Function and Structure 43  \n2 Machine-Learning Foundations: The Probabilistic Framework 47  \n2.1 Introduction: Bayesian Modeling 47  \n2.2 The Cox Jaynes Axioms 50  \n2.3 Bayesian Inference and Induction 53  \n2.4 Model Structures: Graphical Models and Other Tricks 60  \n2.5 Summary 64  \n3 Probabilistic Modeling and Inference: Examples 67  \n3.1 The Simplest Sequence Models 67  \n3.2 Statistical Mechanics 73  \n4 Machine Learning Algorithms 81  \n4.1 Introduction 81  \n4.2 Dynamic Programming 82  \n4.3 Gradient Descent 83  \n4.4 EM/GEM Algorithms 84  \n4.5 Markov-Chain Monte-Carlo Methods 87  \n4.6 Simulated Annealing 91  \n4.7 Ev","cbCaitBrjjEZDGHL","https://ap.wps.com/l/cbCaitBrjjEZDGHL","pdf",3447408,477,"English","# Series Foreword\n# Preface\n# Introduction\n## Biological Data in Digital Symbol Sequences\n## Genomes—Diversity, Size, and Structure\n## Proteins and Proteomes\n## On the Information Content of Biological Sequences\n## Prediction of Molecular Function and Structure\n# Machine-Learning Foundations: The Probabilistic Framework\n## Introduction: Bayesian Modeling\n## The Cox Jaynes Axioms\n## Bayesian Inference and Induction\n## Model Structures: Graphical Models and Other Tricks\n## Summary\n# Probabilistic Modeling and Inference: Examples\n## The Simplest Sequence Models\n## Statistical Mechanics\n# Machine Learning Algorithms\n## Dynamic Programming\n## Gradient Descent\n## EM/GEM Algorithms\n## Markov-Chain Monte-Carlo Methods\n## Simulated Annealing\n## Evolutionary and Genetic Algorithms\n## Learning Algorithms: Miscellaneous Aspects\n# Neural Networks: The Theory\n## Universal Approximation Properties\n## Priors and Likelihoods\n## Learning Algorithms: Backpropagation\n# Neural Networks: Applications\n## Sequence Encoding and Output Interpretation\n## Prediction of Protein Secondary Structure\n## Prediction of Signal Peptides and Their Cleavage Sites\n## Applications for DNA and RNA Nucleotide Sequences\n## Prediction Performance Evaluation\n# Hidden Markov Models: The Theory\n## Likelihood and Basic Algorithms\n## Learning Algorithms\n## Applications of HMMs: General Aspects\n# Hidden Markov Models: Applications\n## Protein Applications\n## DNA and RNA Applications\n## Advantages and Limitations of HMMs\n# Probabilistic Graphical Models in Bioinformatics\n## The Zoo of Graphical Models in Bioinformatics\n## Markov Models and DNA Symmetries\n## Markov Models and Gene Finders\n## Hybrid Models and Neural Network Parameterization of Graphical Models\n## The Single-Model Case\n## Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction\n# Probabilistic Models of Evolution: Phylogenetic Trees\n## Introduction to Probabilistic Models of Evolution\n## Substitution Probabilities and Evolutionary Rates\n## Data Likelihood\n## Optimal Trees and Learning\n## Parsimony\n## Extensions\n# Stochastic Grammars and Linguistics\n## Introduction to Formal Grammars","[{\"question\":\"What kinds of biological data does the book start with for machine learning modeling?\",\"answer\":\"It begins with biological data represented as digital symbol sequences, then covers genomic diversity, size, and structure, followed by proteins and proteomes as key sequence-related objects for modeling.\"},{\"question\":\"Which probabilistic foundations are emphasized in the machine-learning framework?\",\"answer\":\"The book focuses on Bayesian modeling, the Cox Jaynes axioms, Bayesian inference and induction, and model structures such as graphical models to support complex stochastic sequence models.\"},{\"question\":\"How does the book connect models to biological prediction tasks?\",\"answer\":\"It develops methods such as neural networks, hidden Markov models, probabilistic graphical models, and phylogenetic tree models, and relates them to prediction of molecular function and structure, protein secondary structure, signal peptides, and DNA/RNA nucleotide sequence tasks with performance evaluation.\"}]","Bioinformatics - The Machine Learning Approach | PDF",1789976076,1202]