2006
DOI: 10.1109/titb.2005.862466
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Biological Data Warehousing System for Identifying Transcriptional Regulatory Sites From Gene Expressions of Microarray Data

Abstract: Abstract-Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a mi… Show more

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Cited by 3 publications
(3 citation statements)
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“…The data in the warehouse are filtered, aggregated and stored in smaller data storages, usually called data marts (DM), properly designed for specialised purposes. A DW is frequently used in business applications but in the last years it is often used also in the biomedical (especially clinical) domain [41-44]. The choice of a DW for BEAT data management was driven by the following aspects:…”
Section: Methodsmentioning
confidence: 99%
“…The data in the warehouse are filtered, aggregated and stored in smaller data storages, usually called data marts (DM), properly designed for specialised purposes. A DW is frequently used in business applications but in the last years it is often used also in the biomedical (especially clinical) domain [41-44]. The choice of a DW for BEAT data management was driven by the following aspects:…”
Section: Methodsmentioning
confidence: 99%
“…In general, data warehousing is suited for applications with predictable queries, applications requiring high query performance, and applications needing private copies of the data. Biomedical database integration systems that use the data warehousing approach include IGD (Ritter et al 1994), GIMS (Cornell et al 2001), GUS (Davidson et al 2001), DoTS (http://www.allgenes.org/), Qu et al (2002), and Tsou et al (2006).…”
Section: Data Warehousingmentioning
confidence: 99%
“…Analysis of genetic patient data helps in guiding diagnostic and therapeutic work, especially in determining between different causes of diseases with rather similar findings and signs on macroscopic level [1,13,[2][3][4]. Substantial work based on differential coexpression analysis of microarray data [5,6,13] will increase the understanding of cancer and will hopefully lead to improvements in diagnosis and treatment. However, with the increasing use of vast amounts of existing data researchers have to pay attention to several traps caused by misfits of original and current purposes of referred data.…”
Section: Introductionmentioning
confidence: 99%