Learning to combine distances for complex representations
Proceedings of the 24th international conference on Machine learning
Prototypes Based Relational Learning
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after time-consuming annotation by domain experts who must first of all determine the fingerprint type, i.e., whether a fingerprint depicts a protein family, superfamily or domain. To alleviate the annotation bottleneck, a system called PRECIS has been developed which automatically generates prints records, provisionally stored in a supplement called preprints. One limitation of PRECIS is that its classification heuristics, handcoded by proteomics experts, often misclassify fingerprint type; their error rate has been estimated at 40%. This paper reports on an attempt to build more accurate classifiers based on information drawn from the fingerprints themselves and from the SWISS-PROT database. Extensive experimentation using 10-fold cross-validation led to the selection of a model combiing the ReliefF feature selector with an SVM-RBF learner. The final models error rate was estimated at 14.1% on a blind test set, representing a 26% accuracy gain over PRECIS handcrafted rules.