A Protein Classification Benchmark collection for machine learning

URL:

http://hydra.icgeb.trieste.it/benchmark/

What you can do:

Use standard datasets to compare protein classification by different machine learning methods.

Highlights:

This database was created in order to provide standard datasets on which the performance of machine learning methods can be compared.
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.

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Literature & Tutorials:

This record last updated: 03-21-2007