C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Clustering Techniques in Biological Sequence Analysis
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Artificial Intelligence in Medicine
Genetic programming for protein related text classification
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Hybrid Evolutionary Approach for the Protein Classification Problem
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
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Proteins can be grouped into families according to their biological functions. This paper presents a system, named GAMBIT, which discovers motifs (particular sequences of amino acids) that occur very often in proteins of a given family but rarely occur in proteins of other families. These motifs are used to classify unknown proteins, that is, to predict their function by analyzing the primary structure. To search for motifs in proteins, we developed a GA with specially tailored operators for the problem. GAMBIT was compared with MEME, a web tool for finding motifs in the TransMembrane Protein DataBase. Motifs found by both methods were used to build a decision tree and classification rules, using, respectively, C4.5 and Prism algorithms. Motifs found by GAMBIT led to significantly better results, when compared with those found by MEME, using both classification algorithms.