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  d-matrix – database exploration, visualization and analysis

Seelow, D., Galli, R., Mebus, S., Sperling, H.-P., Lehrach, H., & Sperling, S. (2004). d-matrix – database exploration, visualization and analysis. BMC Bioinformatics, 5, 168-168. doi:10.1186/1471-2105-5-168.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8791-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8792-F
Genre: Journal Article

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 Creators:
Seelow, Dominik1, Author              
Galli, Raffaello2, Author
Mebus, Sigrun, Author
Sperling, Hans-Peter, Author
Lehrach, Hans3, Author              
Sperling, Silke3, Author              
Affiliations:
1Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433557              
2Max Planck Society, ou_persistent13              
3Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              

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 Abstract: Background Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis. Results We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis. Conclusions d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data.

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Language(s): eng - English
 Dates: 2004-10-28
 Publication Status: Published in print
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Title: BMC Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 5 Sequence Number: - Start / End Page: 168 - 168 Identifier: -