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Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
(source: Nielsen Book Data)

Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
(source: Nielsen Book Data)

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 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.
(source: Nielsen Book Data)

• Machine learning foundations - the probabilistic framework
• probabilistic modelling and inference - examples
• machine learning algorithms
• neural networks - the theory
• neural networks -applications
• hidden Markov models - the theory
• hidden Markov models - applications
• hybrid systems - hidden Markov Models and neural networks
• probabilistic models of evolution - phylogenetic trees
• stochastic grammars and linguistics
• Internet resources and public databases. Statistics
• information, theory, entropy, and relative entropy
• probabilistic graphical models
• HMM technicalities, scaling, periodic architectures, state functions, and Dirichlet mixtures
• list of main symbols and abbreviations.
• (source: Nielsen Book Data)

The authors of this text present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students: firstly those biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function; and secondly those with a primary background in physics, mathematics, statistics or computer science who need to know more about specific applications in molecular biology.
(source: Nielsen Book Data)

• Introduction, H. Bohr and S. Brunak Overview Three Paradoxes of Protein Folding, P.G. Wolynes Protein-Ligand Complexes and Protein Biosynthesis Conserved Water Molecules and Protein Folding in Fungal Peroxidases, S. Larsen and J.F.W. Petersen Interplay between Metal Coordination Geometry and Protein Structure, R. Bauer, M. Bjerrum, E. Danielsen, E. Friis, J.M. Hammerstad, L. Hemmingsen, M.V. Pedersen, and J. Ulstrup Elongation Factor Tu: A G-Protein in Protein Biosynthesis, P. Nissen, M. Kjeldgaard, S. Thirup, L. Reshetnikova, G. Polekhina, B.F.C. Clark and J. Nyborg Electron Transfer of the Di-Heme Protein: Pseudomonas stutzeri cytochrome c4, , J.-J. Karlsson, A. Kadziola, A. Rasmussen, T.E. Rostrup, and J. Ulstrup Profile Methods for Protein Structure and Fold Determination Sequence Matching in Homology Modelling, M. Gribskov Screening Genome Sequences for Known Folds, M. Braxenthaler and M.J. Sippl Protein Fold Determination Using a Small Number of Distance Restraints, A. Aszodi, M.J. Gradwell, and W.R. Taylor How Many Protein Fold Classes Are To Be Found?, P-A. Lindgard and H. Bohr Distance-Based Protein Structure Prediction and Evaluation The Effect of a Distance-Cutoff on the Performance of The Distance Matrix Error When Used as a Potential Function to Drive Conformational Search, S. Le Grand, A. Elofsson, and D. Eisenberg Protein Structure Prediction: How Self-Misleading Can Be Avoided, A. Ya. Badretdinov, A.M. Gutin, and A.V. Finkelstein Sequence Space Analysis: Identification of Functionally or Structurally Important Residues in Protein Sequence Families, G. Casari, C. Sander, and A. Valencia Fitting 1-D Predictions into 3-D Structures, B. Rost Determination of Membrane Bound Protein Structure and Function Modelling a-helical Integral Membrane Proteins, D. Donnelly and J.B.C. Findlay Use of Small Organic Compounds and Metal-ions as Structural and Functional Probes in 7TM Receptors, T.W. Schwartz, C.E. Elling, S.M. Nielsen, K. Thirstrup, S. Zoffmann, M. Rosenkilde, D. Marchal, R. den Hollander, U. Gether, and S.A. Hjorth Using Sequence Information and Model Building to Explore Subtype Specificity in GPCRs, R. Bywater, G. Vriend, L. Oliveira, and D. van Aalten Statistical Mechanics and Kinetics of Protein Folding Processes Proposed Rules of the Protein Folding Game, M. Crippen and V.N. Maiorov Protein Folding Studied by Monte Carlo Simulations, A. Sali, E. Shakhnovich, and M. Karplus Folding Kinetics of Protein Like Heteropolymers, N.D. Socci and J.N. Onuchic Energy Landscape and Folding Mechanisms in Proteins, Z. Guo and D. Thirumalai Topological Aspects of Protein Folds Resonator Driven Protein Folding: The Implication of Topology for Protein Structure and Folding, J. Bohr, H. Bohr, and S. Brunak Chirality in Protein Structure, T.W.F. Slidel and J.M. Thornton Packing within and between Subunits Defined by Internal Cavities, S.J. Hubbard and P. Argos Protein Modelling and Docking Modelling and Predicting a-helical Transmembrane Structures, W.R. Taylor and D.T. Jones HIV GP120 Docking Interactions and Inhibitor Design Based on an Atomic Structure Derived by Molecular Modelling Using the DREIDING II Force Field, J.L. Gabriel and W.M. Mitchell A Model of the 3D Structure of Obelin-The Photoprotein From Obelia longissima, T. Sandalova Index.
• (source: Nielsen Book Data)

Written by outstanding scientists in physics and molecular biology, this book addresses the most recent advances in the analysis of the protein folding processes and protein structure determination. Emphasis is also placed on modelling and presentation of experimental results of structural membrane bound proteins. Many color plates help to illustrate structural aspects covered including: defining folds of protein domains; structure determination from sequence; distance geometry; lattice theories; membrane proteins; protein-ligand interaction; topological considerations; and, docking onto receptors. All analysis is presented with proven theory and experimentation. "Protein Folds: A Distance-Based Approach" is an excellent text/reference for biotechnologists and biochemists as well as graduate students studying in the research sciences.
(source: Nielsen Book Data)

This book presents contributions from distance-based approches to the generation of detailed 3-dimensional structures of proteins. Insights into protein folding obtained by traditional X-ray, crystallography, circular dicroism and ultra-violet methods are reviewed and new methods incorporating distance-based energy functions may be used for identifying and building protein folds. A section describes the use of neural networks and knowledge-based approaches in determination of fold-class predictions and protein secondary structures.
(source: Nielsen Book Data)