A protein classification benchmark collection for machine learning

Paolo Sonego, Mircea Pacurar, Somdutta Dhir, Attila Kertész-Farkas, András Kocsor, Zoltán Gáspári, Jack A.M. Leunissen, Sándor Pongor

Research output: Article

37 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)D232-D236
JournalNucleic acids research
Volume35
Issue numberSUPPL. 1
DOIs
Publication statusPublished - jan. 2007

ASJC Scopus subject areas

  • Genetics

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    Sonego, P., Pacurar, M., Dhir, S., Kertész-Farkas, A., Kocsor, A., Gáspári, Z., Leunissen, J. A. M., & Pongor, S. (2007). A protein classification benchmark collection for machine learning. Nucleic acids research, 35(SUPPL. 1), D232-D236. https://doi.org/10.1093/nar/gkl812