Search Computing
Challenges and Directions

Stefano Ceri, Marco Brambilla (Eds.)

Springer LNCS, Vol. 5950, March 2010.

[SeCo Book Intro]
[Table of Contents]
[Book preview]

Chapter 15

Search Computing and the Life Sciences

by Marco Masseroli 1, Norman W Paton 2, Irena Spasić 2

1 Dipartimento di Elettronica e Informatzione, Politecnico di Milano,
Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
masseroli@elet.polimi.it
2 School of Computer Science, University of Manchester,
Oxford Road, Manchester M13 9PL, UK.
{npaton, i.spasic}@manchester.ac.uk

Search Computing has been proposed to support the integration of the results of search engines with other data and computational resources. A key feature of the resulting integration platform is direct support for multidomain ordered data, reflecting the fact that search engines produce ranked outputs, which should be taken into account when the results of several requests are combined. In the life sciences, there are many different types of ranked data. For example, ranked data may represent many different phenomena, including physical ordering within a genome, algorithmically assigned scores that represent levels of sequence similarity, and experimentally measured values such as expression levels. This chapter explores the extent to which the search computing functionalities designed for use with search engine results may be applicable for different forms of ranked data that are encountered when carrying out data integration in the life sciences. This is done by classifying different types of ranked data in the life sciences, providing examples of different types of ranking and ranking integration needs in the life sciences, identifying issues in the integration of such ranked data, and discussing techniques for drawing conclusions from diverse rankings.

[SeCo Book Intro]
[Table of Contents]
[Book preview]

 
 
   
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