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Over the past several years our group has focused on the development of bioinformatics, and pattern finding tools to improve the information extraction from large biological data sets, such as transcriptional profiles, sequences, and metabolic fluxes.
Our approach has been to integrate systematic bioinformatics analysis as an essential component of wet-lab experiments. This allows us to conduct more meaningful experiments, and extract more information about the system, from the resulting data sets. Our development of algorithms for finding putative promoters proceeds hand-in-hand with the design of high throughput experimental techniques for validating these putative promoters. Similarly, hypotheses obtained through pattern finding techniques are used to design large-scale experiments to validate the hypotheses, and simultaneously generate knowledge about systemic phenotypes, such as diabetes.
Pattern Discovery
The central theme of our work is the development and application of novel pattern discovery techniques for the analysis of data being generated by the life sciences community. In particular, we are focused on the application of the IBM Teiresias pattern discovery engine to protein/DNA sequence, physiological, and gene expression data.
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People:
- Michael Hansen
- Adrian Fay
- Orhan Karsligil
Selected Papers:
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