Leif E. Peterson – författare

In the decades since the atomic bombing of Hiroshima and Nagasaki, economic and political trends have opened avenues for radiation research while breakthroughs in molecular biology have shed light on radiation's effect on the human body. This volume comprehensively reviews what is now known about human exposure to ionizing radiation, with emphasis on unifying the scientific disciplines that inform this topic. Today's most widely recognized experts in the field examine four broad areas: * Physics and dosimetry, including the various systems of A-bomb survivor dosimetry, the effect on survivors of subsequent medical radiation, and chromosome aberrations as biomarkers. * Cancer statistics and epidemiology, including a historical review of leukemia risk in A-bomb survivors, the incidence of solid cancer and resulting mortality, and the results of studies of workers exposed to low-level radiation. * Genetics, including the path from radiation exposure to cellular effects, carcinogenesis, and mutagenesis. Experts discuss the interaction between radiation and other cancer risk factors, review models of radiation-induced cancer, and report on other aspects of molecular biology.* Psychological effects of radiation catastrophes--as seen at Hiroshima and Nagasaki, Three Mile Island, and Chernobyl--and consequences of the Atomic Bomb Survivors Relief Law.

Wiley Series in Bioinformatics: Computational Techniques and EngineeringYi Pan and Albert Y. Zomaya, Series Editors Wide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies Proliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more. Classification Analysis of DNA Microarrays provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion. This powerful new resource: Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studiesServes as a historical repository of general use supervised classification methods as well as newer contemporary methodsBrings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the bookDescribes implementation methods that help shorten discovery timesClassification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.