Nucleic Acids Research Advance Access originally published online on November 16, 2006
Nucleic Acids Research 2007 35(Database issue):D232-D236; doi:10.1093/nar/gkl812
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Nucleic Acids Research, 2007, Vol. 35, Database issue D232-D236
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Articles |
A Protein Classification Benchmark collection for machine learning
Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, 34012 Trieste, Italy 1 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1. H-6720 Szeged, Hungary 2 Institute of Chemistry, Eötvös Loránd University Pázmány Péter sétány 1/A, H-1117 Budapest, Hungary 3 Bioinformatics Group, Biological Research Centre Hungarian Academy of Sciences, Temesvári krt. 62, H-6701 Szeged, Hungary 4 Laboratory of Bioinformatics, Wageningen University and Research Centre PO Box 8128, 6700 ET Wageningen, The Netherlands
*To whom correspondence should be addressed. Tel: +39 0403757300; Fax: +39 040226555; Email: pongor{at}icgeb.org
Received August 13, 2006. Revised October 3, 2006. Accepted October 3, 2006.
Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection (http://hydra.icgeb.trieste.it/benchmark) was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  P. Sonego, A. Kocsor, and S. Pongor ROC analysis: applications to the classification of biological sequences and 3D structures Brief Bioinform, May 1, 2008; 9(3): 198 - 209. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. F. Angyan, A. Perczel, S. Pongor, and Z. Gaspari Fast protein fold estimation from NMR-derived distance restraints Bioinformatics, January 15, 2008; 24(2): 272 - 275. [Abstract] [Full Text] [PDF]
|
|