GENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION

This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. Since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the sequences. To generate the fuzzy rules, we have used some modified versions of a common approach. The generated rules are simple and understandable, especially for biologists. To evaluate our fuzzy classifiers, we have used four protein superfamilies from UniProt database. Experimental results show the comprehensibility of generated fuzzy rules with comparable classification accuracy. Bioinformatics(4)is basically conceptualizing biology in terms of macromolecules and applying informatics techniques to understand and organize the information associated with these molecules. It deals primarily with the application of computer and statistical techniques to the management of biological information. Because of the Human Genome Project and other similar efforts, a large number of biological data are regularly collected. It is important to organize and annotate this massive amount of sequential data to maximize its utility. In this regard, DNA sequences are translated into protein sequences using standard bioinformatics tools. Among these is protein sequence classification, which determines the type or group of proteins to which an unknown protein sequence belongs. One of the benefits from this type of category grouping is that molecular analysis can be carried out within a particular superfamily instead of an individual protein sequence. A protein superfamily consists of protein sequence members that are evolutionally related and therefore functionally and structurally relevant to each other. Several approaches dealing with the protein classification problem have been proposed in the past. These include alignment of protein sequences (2), hidden Markov modeling (14), application of artificial neural networks (23, 24, 25), using