Classification Analysis of DNA Microarrays (Hardcover)

Wide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies

Proliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more.

Classification Analysis of DNA Microarrays provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion.

This powerful new resource:

• Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies
• Serves as a historical repository of general use supervised classification methods as well as newer contemporary methods
• Brings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book
• Describes implementation methods that help shorten discovery times

Classification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.

傳統的無監督和監督方法以及新的當代方法的廣泛涵蓋，幫助研究人員應對DNA微陣列研究中分類方法的快速增長。

DNA微陣列研究中不斷增加的分類方法導致相關信息散落在文獻、會議論文和其他地方。本書將許多這些分類方法集結在一個單一的卷中。除了傳統的統計方法外，它還涵蓋了新的機器學習方法，如模糊方法、人工神經網絡、基於演化的遺傳算法、支持向量機、粒子群優化等。

《DNA微陣列的分類分析》提供了高度詳細的偽代碼和豐富的圖形編程功能，以及可直接運行的源代碼。除了傳統和當代分類方法，還提供了輔助工具和數據準備程序，用於標準化和模糊化；通過清晰和模糊的c均值、主成分分析和非線性流形學習進行維度降低；以及通過文本分析和n-gram分析、ANN中的遞歸特徵提取、基於核的方法、集成分類器融合的計算語言學。

這個強大的新資源：
- 提供了關於使用分類分析進行大規模高通量轉錄研究的DNA微陣列的信息
- 作為一個歷史性的存儲庫，包含了常用的監督分類方法以及新的當代方法
- 通過實施書中提供的編程偽代碼和源代碼，使讀者能夠快速了解各種分類方法
- 描述了有助於縮短發現時間的實施方法

《DNA微陣列的分類分析》對於計算機科學、生物信息學、生物統計學、系統生物學和許多相關領域的專業人士和研究生非常有用。