Methodology article
EFICAz2: enzyme function inference by a combined approach enhanced by machine learning
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* Corresponding author: Jeffrey Skolnick skolnick@gatech.edu
- † Equal contributors
1 Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, 30318, USA
2 California Institute for Telecommunications and Information Technology, University of California, San Diego, La Jolla, CA, 92093, USA
BMC Bioinformatics 2009, 10:107 doi:10.1186/1471-2105-10-107
Published: 13 April 2009Additional files
Additional file 1:
Figure S1. Example of Functionally Discriminating Residues (FDRs).
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Additional file 2:
Novel enzyme function annotations of the human proteome by EFICAz2. Excel spreadsheet listing all the three-field or four-field EC numbers assigned by EFICAz2 version 13 to human proteins that were not annotated as enzymes in the Release 47.0 of the KEGG database.
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