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d-matrix – database exploration, visualization and analysis

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons50547

Seelow,  Dominik
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

Galli,  Raffaello
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50409

Lehrach,  Hans
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50492

Sperling,  Silke
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-8791-2
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.