Statistical pattern recognition for macromolecular crystallographers

TL;DR: In this paper, a multivariate data set of known AI4AII6(BO4)6X2 apatites is used as the basis for the study where each AI 4AII 6BO4 6X2 compound is represented as a 29-dimensional vector, where the vector components are discrete scalar descriptors of electronic and crystal structure attributes.

TL;DR: A heuristic algorithm with factor analysis and a local search optimization system for pattern identification problems as applied to large and multivariate aero-geophysical data shows that the use of factor transformation captures the discriminative capacity of the original variables.

TL;DR: TEXTAL takes a model-building approach based on real-space density pattern recognition, similar to how a human crystallographer would work, and concatenates local models into a global model and subjects them to various subsequent refinements to produce a complete protein model automatically.

TL;DR: Automated protein modeling systems like TEXTAL™ are critical to the structural genomics initiative, a worldwide effort to determine the 3D structure of all proteins in a high-throughput mode, thereby keeping up with the rapid growth of genomic sequence databases.

TL;DR: In this paper, the authors describe strategies and tools that help to alleviate this problem and simplify the model-building process, quantify the goodness of fit of the model on a per-residue basis and locate possible errors in peptide and side-chain conformations.

TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.

Abstract: Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

TL;DR: In this article, a survey of elementary applications of probability theory can be found, including the following: 1. Plausible reasoning 2. The quantitative rules 3. Elementary sampling theory 4. Elementary hypothesis testing 5. Queer uses for probability theory 6. Elementary parameter estimation 7. The central, Gaussian or normal distribution 8. Sufficiency, ancillarity, and all that 9. Repetitive experiments, probability and frequency 10. Advanced applications: 11. Discrete prior probabilities, the entropy principle 12. Simple applications of decision theory 15.