Data Mining for Bioinformatics
暫譯: 生物資訊學中的資料探勘

Dua, Sumeet, Chowriappa, Pradeep

Data Mining for Bioinformatics

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 biologica

商品描述(中文翻譯)

涵蓋理論、演算法和方法論，以及資料探勘技術，《Data Mining for Bioinformatics》提供了對於在生物資訊學中應用的資料密集型計算的全面討論。它對生物資訊學的資料探勘應用領域提供了廣泛而深入的概述，幫助來自生物學和計算機科學背景的讀者增強對這一跨學科領域的理解。

本書對於在生物資訊學領域（包括基因組學和蛋白質組學）中用於儲存、分析和提取大型資料庫知識的資料探勘技術、技術和框架提供了權威的覆蓋。它首先描述了生物資訊學的演變，並強調了可以通過資料探勘技術解決的挑戰。介紹了可以在生物資料庫中使用的各種資料探勘技術，文本分為四個部分：

1. 提供該領域演變的完整概述及其與計算學習的交集
2. 描述資料探勘在分析大型生物資料庫中的角色——解釋資料探勘所提供的各種特徵選擇和特徵提取技術的廣度
3. 專注於使用聚類技術的無監督學習概念及其在大型生物資料中的應用
4. 涵蓋使用在生物資訊學中最常用的分類技術的監督學習——解決使用聚類或分類推導的推論所需的驗證和基準測試

本書描述了生物資訊學中顯著提到的各種生物資料庫，並包括了在生物資訊學中使用的先進聚類演算法的應用詳細列表。強調了在生物資料上應用分類時遇到的挑戰。

作者簡介

Sumeet Dua is an Upchurch endowed professor of computer science and interim director of computer science, electrical engineering, and electrical engineering technology in the College of Engineering and Science at Louisiana Tech University. He obtained his PhD in computer science from Louisiana State University in 2002. He has coauthored/edited 3 books, has published over 50 research papers in leading journals and conferences, and has advised over 22 graduate thesis and dissertations in the areas of data mining, knowledge discovery, and computational learning in high-dimensional datasets. NIH, NSF, AFRL, AFOSR, NASA, and LA-BOR have supported his research. He frequently serves as a panelist for the NSF and NIH (over 17 panels) and has presented over 25 keynotes, invited talks, and workshops at international conferences and educational institutions. He has also served as the overall program chair for three international conferences and as a chair for multiple conference tracks in the areas of data mining applications and information intelligence. He is a senior member of the IEEE and the ACM. His research interests include information discovery in heterogeneous and distributed datasets, semisupervised learning, content-based feature extraction and modeling, and pattern tracking.

Pradeep Chowriappa is a research assistant professor in the College of Engineering and Science at Louisiana Tech University. His research focuses on the application of data mining algorithms and frameworks on biological and clinical data. Before obtaining his PhD in computer analysis and modeling from Louisiana Tech University in 2008, he pursued a yearlong internship at the Indian Space Research Organization (ISRO), Bangalore, India. He received his masters in computer applications from the University of Madras, Chennai, India, in 2003 and his bachelor's in science and engineering from Loyola Academy, Secunderabad, India, in 2000. His research interests

作者簡介(中文翻譯)

Sumeet Dua 是路易斯安那科技大學工程與科學學院的 Upchurch 受贈計算機科學教授及計算機科學、電氣工程和電氣工程技術的臨時主任。他於 2002 年在路易斯安那州立大學獲得計算機科學博士學位。他共同編著或編輯了 3 本書，並在領先的期刊和會議上發表了超過 50 篇研究論文，指導了超過 22 篇研究生論文和學位論文，研究領域包括數據挖掘、知識發現和高維數據集中的計算學習。他的研究得到了 NIH、NSF、AFRL、AFOSR、NASA 和 LA-BOR 的支持。他經常擔任 NSF 和 NIH 的小組成員（超過 17 次小組），並在國際會議和教育機構上發表了超過 25 次主題演講、受邀演講和工作坊。他還擔任過三個國際會議的總程序主席，以及多個會議專題的主席，專注於數據挖掘應用和信息智能。他是 IEEE 和 ACM 的資深會員。他的研究興趣包括在異質和分佈數據集中的信息發現、半監督學習、基於內容的特徵提取和建模，以及模式追蹤。

Pradeep Chowriappa 是路易斯安那科技大學工程與科學學院的研究助理教授。他的研究專注於數據挖掘算法和框架在生物和臨床數據上的應用。在 2008 年獲得路易斯安那科技大學計算分析與建模博士學位之前，他在印度班加羅爾的印度空間研究組織（ISRO）進行了一年的實習。他於 2003 年在印度金奈的馬德拉斯大學獲得計算機應用碩士學位，並於 2000 年在印度塞坎德拉巴德的洛約拉學院獲得科學與工程學士學位。他的研究興趣

類似商品

最後瀏覽商品 (20)