EFICAz2: enzyme function inference by a combined approach enhanced by machine learning

doi:10.1186/1471-2105-10-107

BMC Bioinformatics

Volume 10

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Methodology article

EFICAz²: enzyme function inference by a combined approach enhanced by machine learning

Adrian K Arakaki¹^* , Ying Huang²^* and Jeffrey Skolnick¹

¹Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, 30318, USA

²California Institute for Telecommunications and Information Technology, University of California, San Diego, La Jolla, CA, 92093, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2009, 10:107doi:10.1186/1471-2105-10-107


Published:	13 April 2009

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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 EFICAz²version 13 to human proteins that were not annotated as enzymes in the Release 47.0 of the KEGG database.

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