Data Mining Techniques for the Life Sciences. Springer Protocols: Methods in Molecular Biology, Volume 609. Edited by Oliviero Carugo and Frank Eisenhaber. Humana Press. New York: Springer Science and Business Media. $110.00. xii + 407 p.; ill.; subject index. ISBN: 978‐1‐60327‐240‐7. 2010.

2011 ◽  
Vol 86 (4) ◽  
pp. 336-337
Author(s):  
Iddo Friedberg
Keyword(s):  
Data Mining ◽  
New York ◽  
Molecular Biology ◽  
Life Sciences ◽  
Data Mining Techniques ◽  
Business Media

scholarly journals The Genetics Markers of Human Immunoglobulins, By R. Grubb. Springer-Verlag, Berlin-Heidelberg-New York 1970. Volume 9 in the series, Molecular Biology, Biochemistry, and Biophysics, edited by A. Kleinzeller (Philadelphia), G. F. Springer (Evanston), and H. G. Wittmann (Berlin). Bound volume; 24 × 17 cm; XII+ 152 pages; 8 black-and-white illustrations. Subject Index. Price: US $ 11.60. - Statistische Methoden - Ein Soforthelfer Statistical Methods — A quick-reference book By Sachs Lothar (Kiel). Springer Verlag, Berlin-Heidelberg-New York 1970. Paperback; 18 × 11 cm; XII + 104 pages; 5 black-and-white illustrations (graphs), 25 tables, and 1 special plate. Price: DM 8.80 (approximately, US $ 3.00). - Anatomisches Bildwörterbuch der internationalen Nomenklatur — Zweite bearbeitete Auflage Illustrate d Anatomical Dictionary of International Nomenclature — Second Edition, Revised By Feneis Heinz (Tubingen). Georg Thieme Verlag, Stuttgart 1970. First edition 1967. Paperback; 19 × 12 cm; VI + 442 pages; 740 original drawings by Gerhard Spitzer. Approximately 6,500 entries. General Index. Price: DM 14.80 (approximately, US $ 5.00).

1971 ◽  
Vol 20 (4) ◽  
pp. 396-398
Keyword(s):  
New York ◽  
Molecular Biology ◽  
Statistical Methods ◽  
Reference Book ◽  
Black And White ◽  
General Index ◽  
Human Immunoglobulins ◽  
Statistische Methoden ◽  
Original Drawings

Parallel, Distributed, and Grid-Based Data Mining

2011 ◽  
pp. 90-119
Author(s):  
Moez Ben HajHmida ◽  
Antonio Congiusta
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Life Sciences ◽  
Business Organizations ◽  
Data Mining Techniques ◽  
Software And Hardware ◽  
Mining Methods ◽  
High Performance Computers ◽  
Grid Based ◽  
Major Data

Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data being collected and for the complexity of the analysis that need to be performed on it. Classic data mining techniques, developed for centralized sites, often reveal themselves inadequate, due to some unique characteristics of today’s data sources. In such cases, sequential approaches to data mining cannot provide for scalability, in terms of the data dimensionality, size, and runtime performance. Moreover, the increasing trend towards decentralized business organizations, distribution of users, software, and hardware systems magnifies the need for more advanced and flexible approaches and solutions. Life science is one of the application areas that best resemble such scenario. This chapter presents the state of the art about the major data mining techniques, systems and approaches. A detailed taxonomy is drawn by analyzing and comparing parallel, distributed and Grid-based data mining methods, with a particular focus on the exploitation of large and remotely dispersed datasets and/or high-performance computers.

scholarly journals Data mining techniques for the life sciences

2011 ◽  
Vol 76 (4) ◽  
pp. 494-494
Author(s):  
G. Ya. Wiederschain
Keyword(s):  
Data Mining ◽  
Life Sciences ◽  
Data Mining Techniques

Parallel, Distributed, and Grid-Based Data Mining

2012 ◽  
pp. 203-231 ◽  
Author(s):  
Moez Ben HajHmida ◽  
Antonio Congiusta
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Life Sciences ◽  
Business Organizations ◽  
Data Mining Techniques ◽  
Software And Hardware ◽  
Mining Methods ◽  
High Performance Computers ◽  
Grid Based ◽  
Major Data

Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data being collected and for the complexity of the analysis that need to be performed on it. Classic data mining techniques, developed for centralized sites, often reveal themselves inadequate, due to some unique characteristics of today’s data sources. In such cases, sequential approaches to data mining cannot provide for scalability, in terms of the data dimensionality, size, and runtime performance. Moreover, the increasing trend towards decentralized business organizations, distribution of users, software, and hardware systems magnifies the need for more advanced and flexible approaches and solutions. Life science is one of the application areas that best resemble such scenario. This chapter presents the state of the art about the major data mining techniques, systems and approaches. A detailed taxonomy is drawn by analyzing and comparing parallel, distributed and Grid-based data mining methods, with a particular focus on the exploitation of large and remotely dispersed datasets and/or high-performance computers.

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