Книга "Статистическая диагностика для онкологии. Анализ высокомерных данных" является справочным пособием, посвященным методам статистического анализа и проверки данных, созданных с помощью высокопроизводительных методов. В отличие от других книг, данное издание фокусируется на системных подходах, то есть анализ проводится не на основе одного гена или белка, а на основе более или менее сложной биологической сети. Методологически авторы описывают различные современные методы статистического анализа, как наблюдаемые, так и ненаблюдаемые, применяемые к различным масштабным наборам данных из геномных и генетических экспериментов. Кроме того, так как за последние годы наличие достаточной вычислительной мощности сместило внимание от параметрических к непараметрическим методам, в книге используются такие компьютерно-интенсивные подходы, как Bootstrap, Markov Chain Monte Carlo или общие методы ресэмплинга. Наконец, в связи с большим количеством информации, доступной в общедоступных базах данных, включена глава о байесовских методах, которая также предоставляет систематический способ интеграции этой информации. Данное издание будет полезным руководством для математиков, медицинских и фундаментальных научных сообществ.
This ready reference explores different techniques for scientifically analyzing and certifying data from elevated-capacity methods. In distinction from alternative titles, this publication emphasizes systems approaches. Rather than a solitary gene or peptide forming the foundation of drying, the approach encompasses a greater or lesser intricate biological community. On the premise, the remarkably balanced contributions tout a wide scope of cutting-edge supervised and semi-supervised putative components used on various large scale dossiers from genetics research and discovery efforts. Moreover, because recent years have made more accessible robust computational hardware and software, emphasis has migrated away from parametic procedures to notably nonparametic approaches. The plans exposed here employ powerful computer-examples such as Resampling Methods and Markov Model Markov Chain Computations, emphasizing these computationally intensive techniques. Because the remarkable measure of information discerned in free public databases, there is a tutorial section devoted to Bayesian discovery, which likewise provides a consistent method to integrate that data. A useful sourcebook for mathematical scientists and medical researchers and those involved in basic research programs.
This ready reference approaches different methods of statistics for analyzing and checking out data as it pertains to high-speed methods. Instead of singular individual genes or proteins being the foundation of analysis, rather than more or less intricate biological networks. From a comparative perspective, the even-keeled contributions explain a scope of up-to-date supervised and semi-supervised statistics used on different large-scale data sets from genomics or genetic experiments. Moreover, because people have had access to adequate accomplishing power within recent years, attention has shifted from parametric methods to nonparametrics, and the methods introduced in the book embrace such accomplished approaches like Resampling techniques or Markov Chains via Monte Carlo methods or generalized resampling procedures. And ultimately, due to remarkably vast quantities of data obtainable from open databases, there's even a section on Bayesian statistics, which offers a consistent strategy for integrating these pieces of information. An appreciated guide for mathematical scholars and the healthcare and fundamental research communities alike.
Электронная Книга «Statistical Diagnostics for Cancer. Analyzing High-Dimensional Data» написана автором Dehmer Matthias в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9783527665440
Описание книги от Dehmer Matthias
This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
