Vague Set Model for Mining Amino Acid Associations in Peptide Sequences of Mycobacterium Tuberculosis Complex (MTBC).

Abstract

Abstract Huge amount of molecular data generated through experiments all over the world is available in online databases. This provides new opportunities and challenges for structuring, management, and analysis of data. One of the major challenges for analysis is the presence of inherent uncertainty in molecular data. Some fuzzy set-based models for mining amino acid associations patterns in peptide sequences are reported in the literature to deal with the uncertainty arising due to degree of relationship among amino acids present in peptide sequences. The existing approaches for association rule mining are not capable of dealing with the uncertainty arising due to missing amino acids in available peptide sequences and due to partial peptide sequences. In the present paper a vague set approach is proposed for mining amino acid associations’ patterns in the peptide sequences of MTBC. The dataset of peptide sequences of MTBC has been taken from NCBI. This dataset consists of both partial and complete peptide sequences. Appropriate vague membership function has been constructed. The results have been computed by vague set and fuzzy set approaches. Based on the comparison of results obtained by fuzzy set and vague set approach it is observed that the vague set approach shows the significant changes in the results due to presence of partial sequences. The results have been used to obtain association rules, secondary structures, and the physiochemical properties. Such models will be of great use in developing signatures that will provide better insight into the structures, functions, and interactions of proteins.