dc.contributor.author | Gan, Xiangchao | |
dc.contributor.author | Liew, Alan Wee-Chung | |
dc.contributor.author | Yan, Hong | |
dc.contributor.editor | Tang, YY | |
dc.contributor.editor | Wang, SP | |
dc.contributor.editor | Lorette, G | |
dc.contributor.editor | Yeung, DS | |
dc.contributor.editor | Yan, H | |
dc.date.accessioned | 2017-05-03T15:20:33Z | |
dc.date.available | 2017-05-03T15:20:33Z | |
dc.date.issued | 2006 | |
dc.date.modified | 2010-10-27T08:28:51Z | |
dc.identifier.isbn | 9780769525211 | |
dc.identifier.issn | 1051-4651 | |
dc.identifier.doi | 10.1109/ICPR.2006.796 | |
dc.identifier.uri | http://hdl.handle.net/10072/24404 | |
dc.description.abstract | Gene expressions measured using microarrays usually suffer from the missing value problem. Existing missing value imputation algorithms have some limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by a global structure. In addition, these algorithms do not take into account many biological constraints in the imputation procedure. In this paper, we propose a set theoretic framework for missing data imputation. We design our algorithm by taking into consideration the biological characteristic of the data and exploit the local correlation and the global correlation structure adaptively. Experiments show that our algorithm can achieve a significant reduction of error compared with existing methods. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.format.extent | 141846 bytes | |
dc.format.mimetype | application/pdf | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | IEEE Computer Society | |
dc.publisher.place | USA | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofconferencename | 18th International Conference on Pattern Recognition (ICPR 2006) | |
dc.relation.ispartofconferencetitle | 18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS | |
dc.relation.ispartofdatefrom | 2006-08-20 | |
dc.relation.ispartofdateto | 2006-08-24 | |
dc.relation.ispartoflocation | Hong Kong, PEOPLES R CHINA | |
dc.relation.ispartofpagefrom | 842 | |
dc.relation.ispartofpageto | + | |
dc.relation.ispartofvolume | 3 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearchcode | 270201 | |
dc.subject.fieldofresearchcode | 280204 | |
dc.title | Microarray Missing Data Imputation based on a Set Theoretic Framework and Biological Constraints | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
gro.rights.copyright | © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | |
gro.date.issued | 2006 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Liew, Alan Wee-Chung | |