EFICAz 2.5
:: DESCRIPTION
EFICAz2 (Enzyme Function Inference by a Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from six different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation-controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, (iv) recognition of multiple Prosite patterns of high specificity, (v) SVM evaluation of CHIEFc families, and (vi) SVM evaluation of Multiple Pfam enzyme families.
::DEVELOPER
Center for the Study of Systems Biology
:: REQUIREMENTS
- Linux
- Python
:: DOWNLOAD
:: MORE INFORMATION
Citation
Arakaki A, Huang Y and Skolnick J (2009) EFICAz2: enzyme function inference by a combined approach enhanced by machine learning. BMC Bioinformatics 10:107