Computational genome analysis: an introduction

Computational genome analysis: an introduction pdf epub mobi txt 电子书 下载 2026

☆☆☆☆☆
出版者:Springer
作者:Richard C. Deonier
出品人:
页数:535
译者:
出版时间:2005
价格:$ 111.87
装帧:HRD
isbn号码:9780387987859
丛书系列:
图书标签:
  • 生物
  • 生物信息学
  • 基因组学
  • 计算生物学
  • Python
  • R
  • 数据分析
  • 生物统计学
  • NGS
  • 基因组数据
  • 序列分析
想要找书就要到 本本书屋
立刻按 ctrl+D收藏本页
你会得到大惊喜!!

具体描述

Computational Genome Analysis : An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field. This book features: Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation Presentation of fundamentals of probability, statistics, and algorithms Implementation of computational methods with numerous examples based upon the R statistics package Extensive descriptions and explanations to complement the analytical development More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature Exercises at the end of chapters Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method. Simon Tavar?? holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavar??'s research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavar?? is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics. Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.

《计算基因组分析:引论》是一本系统性且深入浅出的学术著作,旨在为读者提供对基因组学这一前沿科学领域的全面了解。这本书从根本上探讨了基因组学研究的历史背景与发展历程,通过详实的资料和严谨的分析解析了现代生物技术的发展脉络。它不仅系统介绍了基因组测序的基本原理,还深入剖析了数据处理、算法应用及其在生命科学中的实际意义。 书中对各类基因组数据进行了全面分类与探讨,内容涵盖了从单个基因到整个基因组结构的层面,强调了这些信息如何推动基础研究和应用领域的发展。作者不仅展示了当前主流的测序技术,如下一代测序(NGS)及其优劣势,还详细讲解了数据分析中的复杂挑战,包括噪声控制、变异检测与功能预测。这些内容为读者提供了宝贵的理论支撑和实践指导。 此外,《计算基因组分析:引论》还特别关注跨学科研究的重要性,强调了生物信息学与统计学在基因组领域的深度融合。书中通过大量实例和案例展示了从基因突变到疾病机制的研究进展,使读者能够直观地理解该领域的实际应用价值。同时,作者深入讨论了数据隐私、伦理问题以及未来发展方向,为读者提供了更为全面的视野。 书籍结构设计科学合理,每一章均紧密关联前文与后文,帮助读者逐步积累知识并建立系统性认知。在技术细节和理论内容交错的中间地带,书中体现了对基因组学研究深度的追求。对于希望拓展自身学术视野或从事相关研究的人来说,这本书无疑是一套宝贵的参考资料,它不仅展示了当前科学前沿,更为未来探索奠定了坚实基础。 该书适合有生物信息学、遗传学或生命科学背景的读者,深入浅出地介绍了该领域的重要知识体系,并通过大量案例和数据支持其理论内容,使学习过程充满吸引力与启发性。通过细致入微的内容呈现,这本书不仅丰富了读者对基因组分析的理解,也激发了他们进一步探索研究的兴趣。总体而言,《计算基因组分析:引论》是一部兼具学术深度和可读性的重要著作,值得广泛推荐与深入阅读。

作者简介

目录信息

读后感

评分☆☆☆☆☆

Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman      Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman Springer Manchestor; 2006; ISBN...

评分☆☆☆☆☆

Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman      Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman Springer Manchestor; 2006; ISBN...

评分☆☆☆☆☆

Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman      Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman Springer Manchestor; 2006; ISBN...

评分☆☆☆☆☆

Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman      Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman Springer Manchestor; 2006; ISBN...

评分☆☆☆☆☆

Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman      Computational Genome Analysis: an Introduction Richard C. Deonier, Simon Tavaré and Michael S. Waterman Springer Manchestor; 2006; ISBN...

用户评价

评分☆☆☆☆☆

评分☆☆☆☆☆

评分☆☆☆☆☆

评分☆☆☆☆☆

评分☆☆☆☆☆

本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度,google,bing,sogou 等

© 2026 onlinetoolsland.com All Rights Reserved. 本本书屋 版权所有