MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection

Autor:	Nung Kion Lee, Dianhui Wang
Rok vydání:	2009
Předmět:	Computer science business.industry Pattern recognition computer.software_genre DNA sequencing Matrix similarity Weighting DNA binding site Motif (music) Artificial intelligence Data mining Sequence motif business Cluster analysis computer
Zdroj:	Advances in Neuro-Information Processing ISBN: 9783642024894 ICONIP (1)
DOI:	10.1007/978-3-642-02490-0_59
Popis:	To detect or discover motifs in DNA sequences, two important concepts related to existing computational approaches are motif model and similarity score. One of motif models, represented by a position frequency matrix (PFM), has been widely employed to search for putative motifs. Detection and discovery of motifs can be done by comparing kmers with a motif model, or clustering kmers according to some criteria. In the past, information content based similarity scores have been widely used in searching tools. In this paper, we present a mismatch-based matrix similarity score (namely, MISCORE) for motif searching and discovering purpose. The proposed MISCORE can be biologically interpreted as an evolutionary metric for predicting a kmer as a motif member or not. Weighting factors, which are meaningful for biological data mining practice, are introduced in the MISCORE. The effectiveness of the MISCORE is investigated through exploring its separability, recognizability and robustness. Three well-known information contentbased matrix similarity scores are compared, and results show that our MISCORE works well.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e13784c2c7f0ceb9afa08c28a15e3e17 https://doi.org/10.1007/978-3-642-02490-0_59 Zobrazit plný text záznamu