Michelle L. Green, Ph.D.

E-mail: 
Phone: 650-859-5669
Fax: 650-859-3735
Bioinformatics Scientist
Bioinformatics Research Group
Artificial Intelligence Center
SRI International
333 Ravenswood Ave.
Menlo Park, CA 94025

Curriculum vitae
Resume


Research

PHFiller (Pathway Hole Filler)

A Description of PHFiller
PHFiller is a tool developed by the Bioinformatics Research Group at SRI for identifying and evaluating candidates enzymes for filling pathway holes in Pathway/Genome Databases (PGDBs). A pathway hole is a reaction in a predicted pathway for which no enzyme has been identified in the organism's genome annotation. PHFiller uses BLAST to identify candidate enzymes to fill pathway holes and a Bayesian classifier to determine the probability that each candidate has the function required to fill a particular pathway hole.

Validated PHFiller Predictions
Predictions of protein function made by PHFiller for which experimental confirmation have been independently made. In some cases, our predictions were used to identify hypotheses for testing and these predictions were subsequently confirmed. In other cases, the function was experimentally verified prior to our prediction, but was not included in the genome annotation (or in the development of our prediction).
Pathway Hole
Organism
Predicted Enzyme
Validation Details/References
Cystathionine gamma-lyase
Mycobacterium tuberculosis, H37Rv
Rv1079
J Biol Chem. 2005 Mar 4;280(9):8069-78
Alkane-1-monooxygenase
Mycobacterium tuberculosis, H37Rv Rv3252c
J Bacteriol. 2002 Mar;184(6):1733-42.




Publications

Green, ML and Karp, PD. The Outcomes of Pathway Database Computations Depend on Pathway Ontology. Nucleic Acids Research 2006, 34:13, 3687-3697.

Green, ML and Karp, PD. Genome Annotation Errors in Pathway Databases Due to Semantic Ambiguity in Partial EC Numbers.
Nucleic Acids Research 2005, 33:13, 4035-4039.

Romero, P, Wagg, J, Green, ML, Kaiser, D, Krummenacker, M, and Karp, PD. Computational prediction of human metabolic pathways from the complete human genome. Genome Biology 2004, 6:R2.

Green, ML and Karp, PD. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 2004, 5:76.

Green, ML 2003. A Bayesian Method for Identifying Missing Enzymes in Pathway/Genome Databases. Biomedical Informatics Program. Stanford, CA, Stanford University: 22.

Green, ML and Klein, TE. A Multidomain TIGR/Olfactomedin Protein Family with Conserved Structural Similarity in the N-terminal Region and Conserved Motifs in the C-terminal Region. Mol Cell Proteomics 2002 1:394-403.


Michelle L. Green < >
Last modified: 20-Jun-06







I love my work...