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Learning Phenotype Structure Using Sequence Model

contributor authorYuhai Zhao
contributor authorGuoren Wang
contributor authorXiang Zhang
contributor authorYu, Jeffrey Xu
contributor authorZhanghui Wang
date accessioned2020-03-12T18:23:50Z
date available2020-03-12T18:23:50Z
date issued2014
identifier issn1041-4347
identifier other6522406.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/957091?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleLearning Phenotype Structure Using Sequence Model
typeJournal Paper
contenttypeMetadata Only
identifier padid7989268
subject keywordsbiology computing
subject keywordscomputational complexity
subject keywordsdata mining
subject keywordslearning (artificial intelligence)
subject keywordsmolecular biophysics
subject keywordsFINDER algorithm
subject keywordsNP-complete problem
subject keywordsbiological significance
subject keywordsexpression pattern
subject keywordsexpression signature
subject keywordsg*-sequence model
subject keywordsgene expression data sets
subject keywordsgene ordered expression values
subject keywordsmicroarray data analysis
subject keywordsmicroarray technologies
subject keywordsphenotype structure discovery
subject keywordsphenotype structure learning
subject keywordspruning strategies
subject keywordssequence model
subject keywordsstatistical significance
subject keywordstrivial g*-sequences ident
identifier doi10.1109/TKDE.2013.31
journal titleKnowledge and Data Engineering, IEEE Transactions on
journal volume26
journal issue3
filesize2510907
citations0


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