Machine Learning in Bioinformatics

Yanqing Zhang, Jagath C. Rajapakse

  • 出版商: Wiley
  • 出版日期: 2008-12-03
  • 定價: $4,260
  • 售價: 9.5$4,047
  • 語言: 英文
  • 頁數: 456
  • 裝訂: Hardcover
  • ISBN: 0470116625
  • ISBN-13: 9780470116623
  • 相關分類: Machine Learning生物資訊 Bioinformatics
  • 立即出貨 (庫存=1)

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商品描述

An introduction to machine learning methods and their applications to problems in bioinformatics

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.

From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.

Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

商品描述(中文翻譯)

機器學習方法及其在生物信息學問題中的應用介紹

機器學習技術在計算生物學和生物信息學問題中的應用越來越廣泛。新穎的計算技術用於分析高通量數據,包括序列、基因和蛋白質表達、通路和圖像,對於理解疾病和未來藥物發現至關重要。機器學習技術,如馬爾可夫模型、支持向量機、神經網絡和圖模型,因其處理數據噪聲的隨機性和不確定性以及泛化能力而在生命科學數據分析中取得成功。

《生物信息學中的機器學習》是一本由國際知名研究人員組成的專家小組編輯的書籍,匯總了機器學習方法在生物信息學中應用的最新進展。內容包括:基因組學和蛋白質組學數據挖掘的特徵選擇;比較基因選擇和微陣列數據分類中的變量選擇方法;模糊基因挖掘;基於序列的蛋白質殘基屬性預測;生物序列中長程特徵的概率方法等等。

《生物信息學中的機器學習》對於計算機科學家、工程師、生物學家、數學家、研究人員、臨床醫生、醫學信息學家來說是一個不可或缺的資源。對於高年級本科生和研究生的計算機科學、工程和生物學課程,它也是一本有價值的參考書。