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Fundamentals of Data Mining in Genomics and Proteomics
. Ed(S): Dubitzky, Werner; Granzow, Martin; Berrar, Daniel P.
€ 127.93
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Description for Fundamentals of Data Mining in Genomics and Proteomics
Hardback. Presents analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. This title adopts an approach focusing on concepts and applications and presents analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations. Editor(s): Dubitzky, Werner; Granzow, Martin; Berrar, Daniel P. Num Pages: 281 pages, 68 black & white illustrations, 22 black & white tables, biography. BIC Classification: GPH; PSAK. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 19. Weight in Grams: 606.
As natural phenomena are being probed and mapped in ever-greater detail, scientists in genomics and proteomics are facing an exponentially growing vol ume of increasingly complex-structured data, information, and knowledge. Ex amples include data from microarray gene expression experiments, bead-based and microfluidic technologies, and advanced high-throughput mass spectrom etry. A fundamental challenge for life scientists is to explore, analyze, and interpret this information effectively and efficiently. To address this challenge, traditional statistical methods are being complemented by methods from data mining, machine learning and artificial intelligence, visualization techniques, and emerging technologies such as Web services and grid computing. There exists ... Read more
As natural phenomena are being probed and mapped in ever-greater detail, scientists in genomics and proteomics are facing an exponentially growing vol ume of increasingly complex-structured data, information, and knowledge. Ex amples include data from microarray gene expression experiments, bead-based and microfluidic technologies, and advanced high-throughput mass spectrom etry. A fundamental challenge for life scientists is to explore, analyze, and interpret this information effectively and efficiently. To address this challenge, traditional statistical methods are being complemented by methods from data mining, machine learning and artificial intelligence, visualization techniques, and emerging technologies such as Web services and grid computing. There exists ... Read more
Product Details
Format
Hardback
Publication date
2006
Publisher
Springer-Verlag New York Inc. United States
Number of pages
281
Condition
New
Number of Pages
281
Place of Publication
New York, NY, United States
ISBN
9780387475080
SKU
V9780387475080
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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