Mining and analysing spatio-temporal patterns of gene expression in an integrative database framework.
Mining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results. In our particular case we describe a solution that is found in the extension of the Gene Expression Management System (GEMS), i.e. an integrative framework for spatio-temporal organization of gene expression patterns of zebrafish to a framework supporting data mining, data analysis and patterns interpretation As a proof of principle, the GEMS has been equipped with data mining functionality suitable for spatio-temporal tracking, thereby generating added value to the submission of data for data mining and analysis. The analysis of the genetic networks is based on the availability of domain ontologies which dynamically provides meaning to the discovered patterns of gene expression data. Combination of data mining with the already presently available capabilities of GEMS will significantly augment current data processing and functional analysis strategies.
Belmamoune
M
M
Section Imaging and BioInformatics, Leiden Institute of Advanced Computer Science Leiden University, The Netherlands. mounia@liacs.nl
Potikanond
D
D
Verbeek
F J
FJ
D000465:Algorithms; D000818:Animals; D057225:Data Mining; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D018507:Gene Expression Regulation, Developmental; D013223:Statistics as Topic; D013997:Time Factors; D015027:Zebrafish / Q000235:genetics*