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Abstract

The high-throughput technology has led to exponential growth of biological data and information in online databases. This huge data provides new opportunities and challenges for development and application of informatics approaches for extracting and processing new information and knowledge from these databases. One of the major challenges is the presence of inherent uncertainty in this molecular data. The uncertainty arises due to degree of relationship of amino acids present in the sequences with the various parameters like length range, species. The variation in the length of molecular sequences leads to uncertainty in length ranges. The existing algorithms for association rule mining are not completely capable of dealing with this uncertainty. In this paper a fuzzy soft approach has been proposed for mining amino acid fuzzy associations in peptide sequences of Mycobacterium tuberculosis complex (MTBC). The soft sets are employed to model relationship of amino acids with parameters like length range and species etc. The fuzzy set approach is employed to deal with the uncertainty of length ranges. The appropriate membership function has been proposed to model the uncertainty of length ranges. The fuzzy soft associations of amino acid along with their support and confidence have been computed for peptide sequences of MTBC. The results have been compared with the fuzzy approach as well as soft set approach and it is observed that there is significant change in the results. The proposed approach is quite useful in addressing the issue of uncertainty in molecular sequences considered in this paper. The fuzzy soft approach provides visibility of dependence of various characteristics on type of species and length ranges of sequences. Also the amino acid associations information have been generated in the form of rules which can be useful in developing signature which will provide better insights of structure, functions and interactions etc.

Keywords

Association rule support confidence fuzzy set soft set

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Fuzzy soft set model for mining amino acid associations in peptide sequences of Mycobacterium tuberculosis complex (MTBC)

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