Mining fuzzy amino acid associations in peptide sequences of mycobacterium tuberculosis complex (MTBC)
The biological databases are flooded with genomic and proteomic data which can be analyzed to generate the information and knowledge which can be useful for understanding molecular mechanisms involved in disease and health state of a living being. The tuberculosis is an infectious disease and is pandemic, causing large number of deaths every year. In this paper an attempt has been made to develop a model for mining amino acid association patterns in peptide sequences of MTBC. The peptide sequences of species of MTBC are taken from the NCBI. The variation in the length of these sequences leads to variation in degree of relationship among amino acids present in each sequence. The fuzzy set is employed to model this uncertainty of degree of relationships among the amino acids of the peptide sequences of MTBC. The crisp and fuzzy amino acid association rules have been generated from the peptide sequences of MTBC and on comparison it is observed that fuzzy set approach is able to address the issue of under prediction and over prediction of amino acid association patterns due to uncertainty in degree of relationship among the amino acid. The amino acid association patterns have been used to predict secondary structure and physiochemical properties as an illustration. Thus the patterns generated can be useful in understanding the molecular mechanisms involved in MTBC by predicting physiochemical properties, structures and proteinprotein interactions etc.
association rule data mining support confidence all-confidence