C4.5: programs for machine learning
C4.5: programs for machine learning
Prediction of Enzyme Classification from Protein Sequence without the Use of Sequence Similarity
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Integrating knowledge through cooperative negotiation: a case study in bioinformatics
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
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Most of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process --- thus freeing the specialist to carry out more valuable tasks --- has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function.