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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences Hardback - 2011 - 2012th Edition

by Rob Sullivan

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Reader reviews for Introduction to Data Mining for the Life Sciences

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Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.

From the rear cover

One of the major challenges for the scientific community, a challenge that has been seen in many business disciplines, is the exponential increase in data being generated by new experimental techniques and research. A single microarray experiment, for example, can generate thousands of data points that need to be analyzed, and this problem is predicted to increase. As new techniques in areas such as genomics and proteomics continue to be adopted into the mainstream as the costs fall, the need for effective mechanisms for synthesizing these disparate forms of data together for analysis is of paramount importance. But the sheer volume of data means that traditional techniques need to be augmented by approaches that elicit knowledge from the data, using automated procedures.

Data mining provides a set of such techniques, new techniques to integrate, synthesize, and analyze the data, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.

Details

  • Title Introduction to Data Mining for the Life Sciences
  • Author Rob Sullivan
  • Binding Hardback
  • Edition number 2012th
  • Edition 2012
  • Pages 638
  • Volumes 1
  • Language ENG
  • Publisher Humana
  • Publication date 2011-12-26
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • ISBN 9781588299420 / 1588299422
  • Weight 2.24 lbs (1.02 kg)
  • Dimensions 9.2 x 6.1 x 1.6 in (23.37 x 15.49 x 4.06 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Science
  • Library of Congress subjects Data mining, Life sciences - Research - Methodology
  • Library of Congress Catalogue Number 2011941596
  • Dewey Decimal Code 570.285

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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences

by SULLIVAN, Rob

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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences

by Sullivan, Rob

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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences

by Sullivan, Rob

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ISBN 10 / ISBN 13
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Introduction to Data Mining for the Life Sciences 2010
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Introduction to Data Mining for the Life Sciences 2010

by Sullivan, Rob

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INTRODUCTION TO DATA MINING FOR THE LIFE SCIENCES (HB 2012)
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INTRODUCTION TO DATA MINING FOR THE LIFE SCIENCES (HB 2012)

by SULLIVAN R.

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USA Edition . New. Brand New! Fast Delivery US Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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