Statistical Methods in Bioinformatics: An Introduction, 2/e (Hardcover)

Warren J. Ewens, Gregory R. Grant

  • 出版商: Springer
  • 出版日期: 2004-12-21
  • 售價: $5,120
  • 貴賓價: 9.5$4,864
  • 語言: 英文
  • 頁數: 598
  • 裝訂: Hardcover
  • ISBN: 0387400826
  • ISBN-13: 9780387400822
  • 相關分類: 生物資訊 Bioinformatics
  • 海外代購書籍(需單獨結帳)
    無現貨庫存(No stock available)

買這商品的人也買了...

商品描述

Description

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

This book provides an introduction to some of these new methods.  The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes.  The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

 

The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.

 

The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.

 

Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.

 

Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.

 

Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).

Table of Contents

An Introduction to Probability Theory: One Random Variable * An Introduction to Probability Theory: Many Random Variables * Statistics: An Introduction to Statistical Inference * Stochastic Processes: An Introduction to Poisson Processes and Markov Chains * The Analysis of DNA Sequence Patterns: One sequence * The Analysis of DNA Sequences: Multiple sequences * Stochastic Processes: Random Walks * Statistics: Classical Estimation and Hypothesis Testing * BLAST * Stochastic Processes: Markov Chains * Hidden Markov Models * Computationally intensive methods * Evolutionary models * Phylogenetica tree estimation


 

商品描述(中文翻譯)

描述

電腦和生物技術的進步對生物醫學研究產生了深遠的影響,因此現在可以生成複雜的數據集來解決極其複雜的生物學問題。相應地,用於分析此類數據所需的統計方法的進步緊隨數據生成方法的進步。生物信息學所需的統計方法為研究界提出了許多新的和困難的問題。

本書介紹了其中一些新方法。主要涉及的生物學主題包括序列分析、BLAST、微陣列分析、基因預測和演化過程分析。主要涵蓋的統計技術包括假設檢驗和估計、泊松過程、馬爾可夫模型和隱馬爾可夫模型,以及多重檢驗方法。

第二版新增了關於微陣列分析和統計推斷的章節,包括ANOVA的討論,以及基於超幾何分布的模式統計理論和方法的討論。大部分內容已經進行了澄清和重新組織。

本書的寫作旨在吸引希望了解該領域統計方法的生物學家和計算機科學家,以及希望參與生物信息學的訓練有素的統計學家。前幾章以初級水平介紹概率和統計概念,但強調與後續章節相關的材料,通常在標準入門教材中未涵蓋。後續章節對受過訓練的統計學家來說應該是立即可理解的。足夠的數學背景包括微積分和線性代數的入門課程。使用的基本生物概念已經解釋,或者可以從上下文中理解,標準數學概念在附錄中總結。每章末尾提供問題,讓讀者發展主文中概述的理論的各個方面。

關於第一版的評論。"這本書將是研究生課程的理想教材...[並且]是...