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Andrew Keller
Area of Expertise
Computational Biology
Proteomics
Current Position
Senior Research Scientist
Degree
M.S., Computer Science and Engineering, University of Washington
Ph.D., Biochemistry and Molecular Biology, Harvard University
Research Interests
Dr. Keller is interested in modeling biological systems to gain insight.
While studying the regulation of gene transcription, he modeled the behavior of synthetic genetic
circuits to identify those with multiple stable states of gene expression. A current aim is to
apply similar models to biological networks inferred from diverse types of data. Dr. Keller has
also been involved in developing algorithms to facilitate high throughput shotgun proteomics.
He employed machine learning and statistical methods to identify confident peptide assignments
to tandem mass spectra, and from those assignments, derive likely sample proteins.
Selected Publications
Zhang H, Loriaux P, Eng J, Campbell D, Keller A, et al. 2006. UniPep-a database for human N-linked glycosites: a resource for biomarker discovery. Genome Biol 7:R73 Epub Aug 10.
Keller A, Eng J, Zhang N, Li XJ, Aebersold R. 2005. A uniform proteomics ms/ms analysis platform utilizing open xml file formats. Mol Systems Biol Epub 02 Aug.
Nesvizhskii A, Keller A, Kolker E, Aebersold R. 2003. A statistical model for identifying proteins by tandem mass spectrometry. Analytical Chemistry 75: 4646-4658.
Keller A, Nesvizhskii A, Kolker E, Aebersold R. 2002. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Analytical Chemistry 74: 5383-5392.
Keller A, Purvine S, Nesvizhskii A, Stoliar S, Goodlett D, Kolker E. 2002. Experimental protein mixture for validating tandem mass spectral analysis. OMICS 6: 207-212.
Relevant links
Trans-Proteomic Pipeline, including PeptideProphet and ProteinProphet software
http://www.proteomecenter.org/software.php
Seattle Proteome Center
http://sashimi.sourceforge.net/
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