Stock photo: cover may vary
Introduction to Data Mining for the Life Sciences Hardback - 2011 - 2012th Edition
by SULLIVAN, Rob
- Used
- Hardback
A$79.94
A$53.29
Delivery to USA
Standard delivery: 10 to 21 days
More delivery options
Standard delivery: 10 to 21 days
Details
- Title Introduction to Data Mining for the Life Sciences
- Author SULLIVAN, Rob
- Binding Hardback
- Edition number 2012th
- Edition 2012
- Pages 638
- Volumes 1
- Language ENG
- Publisher Humana Press
- Publication date 2011-12-26
- Illustrated Yes
- Features Bibliography, Illustrated, Index
- Bookseller's Inventory # 3238
- 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
- Quantity available 1
About Antiquariaat Looijestijn Netherlands
Biblio member since 2019
Antiquariaat Looijestijn has a shop located in Boekhandel Donner with 12.000 books. Our storage has another 60.000 books, most of them are not online. So be happy to receive special interest or searchlists.
Reader reviews for Introduction to Data Mining for the Life Sciences
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
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.
