《生物信息学数据挖掘》涵盖了理论、算法、方法以及数据挖掘技术，全面讨论了数据挖掘中的数据密集型计算与生物信息学的应用。它提供了一个广泛而深入的生物信息学数据挖掘应用领域的概述，以帮助来自生物学和计算机科学背景的读者增强对这一跨学科领域的理解。

本书对生物信息学领域（包括基因组学和蛋白质组学）中用于存储、分析和从大型数据库中提取知识的数据挖掘技术、技术和框架进行了权威的介绍。本书首先介绍了生物信息学的发展历程，并强调了使用数据挖掘技术可以解决的挑战。介绍了可以在生物数据库中采用的各种数据挖掘技术，正文分为四个部分。

完整地概述了该领域的发展及其与计算学习的交集。

描述了数据挖掘在分析大型生物数据库中的作用--解释了数据挖掘所能提供的各种特征选择和特征提取技术的气息。

重点研究使用聚类技术进行无监督学习的概念及其在大型生物数据中的应用。

涵盖了生物信息学中最常用的分类技术的监督学习--解决了使用聚类或分类得出的推论的验证和基准的需求。

本书介绍了生物信息学中突出提到的各种生物数据库，并详细列举了生物信息学中使用的高级聚类算法的应用。突出强调了在生物数据库上应用分类过程中遇到的挑战，考虑了单分类器和集合分类器的系统，并分享了模型选择和性能估计策略的省力技巧。

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field.

The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections:

Supplies a complete overview of the evolution of the field and its intersection with computational learning

Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer

Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data

Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification

The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.