Exploring Highly Structure Similar Protein Sequence Motifs using SVD with Soft Granular Computing Models

Vital areas in Bioinformatics research is one of the Protein sequence analysis. Protein sequence motifs are determining the structure, function, and activities of the particular protein. The main objective of this paper is to obtain protein sequence motifs which are universally conserved across protein family boundaries. In this research, the input dataset is extremely large. Hence, an efficient technique is demanded. A Rough Granular computing model is created to efficiently extracting protein motif data that transcends protein families. Before apply this model, the very first step of this research is trying to reduce segments. The literature suggests that the Singular Value Decomposition (SVD) computing technique is more suited for reducing segments. After that the reduced segments are followed by applying Rough Granular computing model. The effectiveness of final results effectiveness is tested by several measures. The experimental results suggest that the SVD with Rough Granular computing model generates more number of highly structured motif patterns. KeywordsProtein Sequence Motifs, DBI, DI, HSSP-BLOSUM62, Granular Computing, K-Means, Adaptive Fuzzy C-Means, Rough K-Means.