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- Publisher Website: 10.1109/TCBB.2007.1022
- Scopus: eid_2-s2.0-34547973950
- PMID: 17666761
- WOS: WOS:000248414700008
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Article: Strategies for identifying statistically significant dense regions in microarray data
Title | Strategies for identifying statistically significant dense regions in microarray data |
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Authors | |
Keywords | Microarray Gene expression Dense region Coexpressed genes Bicluster Categorical data Clustering |
Issue Date | 2007 |
Citation | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007, v. 4, n. 3, p. 415-428 How to Cite? |
Abstract | We propose and study the notion of dense regions for the analysis of categorized gene expression data and present some searching algorithms for discovering them. The algorithms can be applied to any categorical data matrices derived from gene expression level matrices. We demonstrate that dense regions are simple but useful and statistically significant patterns that can be used to 1) Identify genes and/or samples of Interest and 2) eliminate genes and/or samples corresponding to outliers, noise, or abnormalities. Some theoretical studies on the properties of the dense regions are presented which allow us to characterize dense regions Into several classes and to derive tailor-made algorithms for different classes of regions. Moreover, an empirical simulation study on the distribution of the size of dense regions is carried out which is then used to assess the significance of dense regions and to derive effective pruning methods to speed up the searching algorithms. Real microarray data sets are employed to test our methods. Comparisons with six other well-known clustering algorithms using synthetic and real data are also conducted which confirm the superiority of our methods in discovering dense regions. The DRIFT code and a tutorial are available as supplemental material, which can be found on the Computer Society Digital Library at http://computer.org/tcbb/archlves. htm. © 2007 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/276814 |
ISSN | 2023 Impact Factor: 3.6 2023 SCImago Journal Rankings: 0.794 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yip, Andy M. | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Wu, Edmond H. | - |
dc.contributor.author | Chan, Tony F. | - |
dc.date.accessioned | 2019-09-18T08:34:44Z | - |
dc.date.available | 2019-09-18T08:34:44Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007, v. 4, n. 3, p. 415-428 | - |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276814 | - |
dc.description.abstract | We propose and study the notion of dense regions for the analysis of categorized gene expression data and present some searching algorithms for discovering them. The algorithms can be applied to any categorical data matrices derived from gene expression level matrices. We demonstrate that dense regions are simple but useful and statistically significant patterns that can be used to 1) Identify genes and/or samples of Interest and 2) eliminate genes and/or samples corresponding to outliers, noise, or abnormalities. Some theoretical studies on the properties of the dense regions are presented which allow us to characterize dense regions Into several classes and to derive tailor-made algorithms for different classes of regions. Moreover, an empirical simulation study on the distribution of the size of dense regions is carried out which is then used to assess the significance of dense regions and to derive effective pruning methods to speed up the searching algorithms. Real microarray data sets are employed to test our methods. Comparisons with six other well-known clustering algorithms using synthetic and real data are also conducted which confirm the superiority of our methods in discovering dense regions. The DRIFT code and a tutorial are available as supplemental material, which can be found on the Computer Society Digital Library at http://computer.org/tcbb/archlves. htm. © 2007 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE/ACM Transactions on Computational Biology and Bioinformatics | - |
dc.subject | Microarray | - |
dc.subject | Gene expression | - |
dc.subject | Dense region | - |
dc.subject | Coexpressed genes | - |
dc.subject | Bicluster | - |
dc.subject | Categorical data | - |
dc.subject | Clustering | - |
dc.title | Strategies for identifying statistically significant dense regions in microarray data | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TCBB.2007.1022 | - |
dc.identifier.pmid | 17666761 | - |
dc.identifier.scopus | eid_2-s2.0-34547973950 | - |
dc.identifier.volume | 4 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 415 | - |
dc.identifier.epage | 428 | - |
dc.identifier.isi | WOS:000248414700008 | - |
dc.identifier.issnl | 1545-5963 | - |