Data Mining for Bioinformatics (Hardcover)

Sumeet Dua, Pradeep Chowriappa

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商品描述

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:

  1. Supplies a complete overview of the evolution of the field and its intersection with computational learning
  2. 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
  3. Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data
  4. 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.

商品描述(中文翻譯)

《生物信息學數據挖掘》涵蓋了理論、算法、方法論以及數據挖掘技術,並應用於生物信息學領域。本書全面討論了數據密集型計算在數據挖掘中的應用,幫助生物學和計算機科學背景的讀者更好地理解這個跨學科領域。

本書權威地介紹了在生物信息學領域中用於存儲、分析和提取知識的數據挖掘技術、技術和框架。首先描述了生物信息學的發展歷程,並強調了可以使用數據挖掘技術解決的挑戰。接著介紹了可以應用於生物數據庫的各種數據挖掘技術,本書分為四個部分:

1. 提供了該領域發展的完整概述,以及與計算學習的交叉領域。
2. 描述了數據挖掘在分析大型生物數據庫中的作用,解釋了數據挖掘提供的各種特徵選擇和特徵提取技術的廣度。
3. 聚焦於使用聚類技術進行無監督學習的概念,以及其在大型生物數據中的應用。
4. 詳細介紹了在生物信息學中最常用的監督學習方法,並討論了使用聚類或分類所得到的推論的驗證和基準的需求。

本書描述了生物信息學中常用的各種生物數據庫,並列出了在生物信息學中使用的高級聚類算法的應用。同時,本書還強調了在生物數據庫上應用分類時遇到的挑戰,並提供了單個和集成分類器系統的模型選擇和性能估計策略的省時技巧。