MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection
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 discov...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Book Chapter |
| Language: | English |
| Published: |
Springer Berlin/Heidelberg
2009
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11923/ http://ir.unimas.my/id/eprint/11923/1/MISCORE_abstract.pdf |
| _version_ | 1848837089432436736 |
|---|---|
| author | Wang, Dianhui Lee, Nung Kion |
| author2 | Köppen, Mario |
| author_facet | Köppen, Mario Wang, Dianhui Lee, Nung Kion |
| author_sort | Wang, Dianhui |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | 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 mismatchbased
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. |
| first_indexed | 2025-11-15T06:34:07Z |
| format | Book Chapter |
| id | unimas-11923 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:34:07Z |
| publishDate | 2009 |
| publisher | Springer Berlin/Heidelberg |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-119232016-05-12T04:03:06Z http://ir.unimas.my/id/eprint/11923/ MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection Wang, Dianhui Lee, Nung Kion QA Mathematics T Technology (General) 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 mismatchbased 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. Springer Berlin/Heidelberg Köppen, Mario Kasabov, Nikola Coghill, George 2009 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/11923/1/MISCORE_abstract.pdf Wang, Dianhui and Lee, Nung Kion (2009) MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection. In: Advances in Neuro-Information Processing. Lecture Notes in Computer Science, 5506 . Springer Berlin/Heidelberg, pp. 478-485. ISBN 978-3-642-02490-0 http://download.springer.com/static/pdf/310/chp%253A10.1007%252F978-3-642-02490-0_59.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-642-02490-0_59&token2=exp=1462501544~acl=%2Fstatic%2Fpdf%2F310%2Fchp%25253A10.1007%25252F978-3-64 10.1007/978-3-642-02490-0_59 |
| spellingShingle | QA Mathematics T Technology (General) Wang, Dianhui Lee, Nung Kion MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection |
| title | MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection |
| title_full | MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection |
| title_fullStr | MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection |
| title_full_unstemmed | MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection |
| title_short | MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection |
| title_sort | miscore: mismatch-based matrix similarity scores for dna motif detection |
| topic | QA Mathematics T Technology (General) |
| url | http://ir.unimas.my/id/eprint/11923/ http://ir.unimas.my/id/eprint/11923/ http://ir.unimas.my/id/eprint/11923/ http://ir.unimas.my/id/eprint/11923/1/MISCORE_abstract.pdf |