Abstract:
TFBSs are known as regulatory motifs and can be represented as position frequency matrices (PFMs). The de novo identification of transcription factor binding sites (TFBSs...Show MoreMetadata
Abstract:
TFBSs are known as regulatory motifs and can be represented as position frequency matrices (PFMs). The de novo identification of transcription factor binding sites (TFBSs) is a crucial problem in computational biology and includes the issue of comparing putative TFBSs to one another and to already known TFBSs. To date there is no fuzzy approach for this problem. In this work we propose the use of fuzzy measures to deal with motif comparison tasks. We investigate the behavior of different classes of classical measures for fuzzy sets including set-theoretic (Jaccard's method), proximity-based (Minkowsky's r-metric), angular coefficient-based (Bhattacharyya's distance) and a measure defined for the fuzzy polynucleotide space. We show that fuzzy measures provide excellent results when dealing with sets of randomly generated motifs and outperforms other existing measures when facing datasets of real motifs.
Published in: 2009 IEEE International Conference on Fuzzy Systems
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584