Integrating Heterogeneous Microarray Data Sources Using Correlation Signatures

Lecture Notes in Computer Science - Data Integration in the Life Sciences ◽

10.1007/11530084_10 ◽

2005 ◽

pp. 105-120 ◽

Cited By ~ 8

Author(s):

Jaewoo Kang ◽

Jiong Yang ◽

Wanhong Xu ◽

Pankaj Chopra

Keyword(s):

Microarray Data ◽

Data Sources

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e2eXpress: END-TO-END BIOINFORMATICS AND KNOWLEDGE MANAGEMENT SYSTEM FOR MICROARRAYS

Journal of Biological System ◽

10.1142/s0218339002000664 ◽

2002 ◽

Vol 10 (04) ◽

pp. 285-302 ◽

Cited By ~ 1

Author(s):

SUDESHNA ADAK

Keyword(s):

Knowledge Management ◽

Microarray Data ◽

Management System ◽

Gene Clusters ◽

Biomedical Literature ◽

Knowledge Management System ◽

Data Sources ◽

Biological Information ◽

Full Potential ◽

End To End

The advent of high-density microarrays has made it possible for scientists to measure the expression levels of thousands of genes simultaneously. Understanding and interpreting the massive volumes of microarray data is necessary to unravel the molecular basis of diseases and will someday lead to medicines tailored for individual genetic profiles. One of the main barriers to realizing the full potential of microarrays today is the need for specialized bioinformatics and knowledge management solutions required to mine the microarray data for biological information. After initial efforts at clustering expression data based on similarity, scientists have recognized the need to cross-reference and correlate experimental data with external data sources, to improve the quality of the biological conclusions that can be drawn. This paper describes e2eXpress, such an end-to-end Bioinformatics and Knowledge Management System for Microarrays. e2eXpress incorporates basic data management and analysis tasks with novel approaches for mining various molecular biological databases to summarize information regarding coregulated gene clusters. In particular, this paper describes two new algorithms: (a) Text Mining for Gene Clusters: a statistical algorithm that is aimed at deriving biologically relevant information for gene clusters from the biomedical literature; (b) Pathway Scoring for Gene Clusters: a computational algorithm that is aimed at deriving pathway related information for gene clusters. This paper describes the variety of statistical and computational algorithms that are required to mine the transcriptome in conjunction with extraneous data sources that can lead to real biological advances.

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