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- My research is about discovering knowledge from data using techniques from machine learning, data mining and statistics.
- My focus is on applications in computational biology and genomics where the need for fast and reliable ways to make sense of the flood of data from modern, high-throughput biological experiments is extremely acute.
- I developed and support two highly-utilized tools for biological sequence analysis: MEME (for motif discovery) and MAST (for scanning sequences with motifs).
- Other tools I have helped develop include Meta-MEME and MCAST for motif-based sequence modeling, various tools for predicting protein properties, structure and family membership, and GONOME, a tool for finding associations between genomic positions and gene annotation classes.
- Hallmarks of my research are making software tools available to users via free web interfaces, and developing statistical methods for insuring the reliability of predictions made by the algorithms.
- Current projects include developing static models of the transcriptional output of genes; studying the function of small RNA in human cells; exploring the regulation of genes involved in the development of red blood cells; predicting the targets of transcription factors using methods combining models DNA of evolution with models of DNA binding.