Semantic web reasoning for analyzing gene expression profiles

Authors:
Liviu Badea
Affiliations:
AI Lab, National Institute for Research and Development in Informatics, Bucharest, Romania
Venue:
PPSWR'06 Proceedings of the 4th international conference on Principles and Practice of Semantic Web Reasoning
Year:
2006

Citing 6
Cited 1

Magic sets and other strange ways to implement logic programs (extended abstract)

PODS '86 Proceedings of the fifth ACM SIGACT-SIGMOD symposium on Principles of database systems
Mediators in the Architecture of Future Information Systems

Computer
Logical foundations of object-oriented and frame-based languages

Journal of the ACM (JACM)
Managing semistructured data with florid: a deductive object-oriented perspective

Information Systems - Special issue on semistructured data
TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web

ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Xcerpt and visXcerpt: from pattern-based to visual querying of XML and semistructured data

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29

Bridging concrete and abstract syntaxes in model-driven engineering: a case of rule languages

Software—Practice & Experience

Quantified Score

Hi-index	0.00

Visualization

Abstract

We argue that Semantic Web reasoning is an ideal tool for analyzing gene expression profiles and the resulting sets of differentially expressed genes produced by high-throughput microarray experiments, especially since this involves combining not only very large, but also semantically and structurally complex data and knowledge sources that are inherently distributed on the Web. In this paper, we describe an initial implementation of a full-fledged system for integrated reasoning about biological data and knowledge using Sematic Web reasoning technology and apply it to the analysis of a public pancreatic cancer dataset produced in the Pollack lab at Stanford.