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dc.contributor.authorZhang, Shuzhongzh_CN
dc.contributor.authorWang, Kunzh_CN
dc.contributor.authorAshby, Codyzh_CN
dc.contributor.authorChen, Bilianzh_CN
dc.contributor.authorHuang, Xiuzhenzh_CN
dc.contributor.author陈碧连zh_CN
dc.date.accessioned2015-07-22T02:39:46Z
dc.date.available2015-07-22T02:39:46Z
dc.date.issued2012zh_CN
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012:59-70zh_CN
dc.identifier.other20124615674821zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86763
dc.descriptionConference Name:7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012. Conference Address: Tokyo, Japan. Time:November 8, 2012 - November 10, 2012.zh_CN
dc.descriptionInternational Association for Pattern Recognition (IAPR); Tokyo Institute of Technology; Japanese Society for Bioinformatics (JSBi)zh_CN
dc.description.abstractHigh-throughput techniques are producing large-scale high-dimensional (e.g., 4D with genes vs timepoints vs conditions vs tissues) genome-wide gene expression data. This induces increasing demands for effective methods for partitioning the data into biologically relevant groups. Current clustering and co-clustering approaches have limitations, which may be very time consuming and work for only low-dimensional expression datasets. In this work, we introduce a new notion of 'co-identification', which allows systematical identification of genes participating different functional groups under different conditions or different development stages. The key contribution of our work is to build a unified computational framework of co-identification that enables clustering to be high-dimensional and adaptive. Our framework is based upon a generic optimization model and a general optimization method termed Maximum Block Improvement. Testing results on yeast and Arabidopsis expression data are presented to demonstrate high efficiency of our approach and its effectiveness. 漏 2012 Springer-Verlag.zh_CN
dc.language.isoen_USzh_CN
dc.publisherSpringer Verlagzh_CN
dc.source.urihttp://dx.doi.org/10.1007/978-3-642-34123-6_6zh_CN
dc.subjectFunctional groupszh_CN
dc.subjectGene expressionzh_CN
dc.subjectPattern recognitionzh_CN
dc.subjectTissuezh_CN
dc.titleA unified adaptive co-identification framework for high-D expression datazh_CN
dc.typeConferencezh_CN


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