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A New Unsupervised Binning Approach for Metagenomic Sequences Based on N-grams and Automatic Feature Weighting

contributor authorRuiqi Liao
contributor authorRuichang Zhang
contributor authorJihong Guan
contributor authorShuigeng Zhou
date accessioned2020-03-12T18:31:40Z
date available2020-03-12T18:31:40Z
date issued2014
identifier issn1545-5963
identifier other6654133.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/961461?show=full
formatgeneral
languageEnglish
publisherIEEE
titleA New Unsupervised Binning Approach for Metagenomic Sequences Based on N-grams and Automatic Feature Weighting
typeJournal Paper
contenttypeMetadata Only
identifier padid7994263
subject keywordsbiology computing
subject keywordsfeature extraction
subject keywordsgenomics
subject keywordsmicroorganisms
subject keywordssensitivity
subject keywordssequences
subject keywordsunsupervised learning
subject keywordsAbundanceBin
subject keywordsF-measure
subject keywordsMCluster
subject keywordsMetaCluster 3.0
subject keywordsMetaCluster 5.0
subject keywordsN-grams
subject keywordsautomatic feature weighting
subject keywordsbasic K-means clustering algorithm
subject keywordshigh-throughput technologies
subject keywordsmetagenomic analysis
subject keywordsmetagenomic data binning
subject keywordsmetagenomic sequences
subject keywordsreal data set
subject keywordssampled microbial community
subject keywordssensitivity
subject keywordssequence feature extraction
subject keywordsshort metagenomic reads
subject keywordssimulated data sets
subject keywordstaxonomical
identifier doi10.1109/TCBB.2013.137
journal titleComputational Biology and Bioinformatics, IEEE/ACM Transactions on
journal volume11
journal issue1
filesize2394154
citations0


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