A New Unsupervised Binning Approach for Metagenomic Sequences Based on N-grams and Automatic Feature Weighting
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: 2014DOI: 10.1109/TCBB.2013.137
Keyword(s): biology computing,feature extraction,genomics,microorganisms,sensitivity,sequences,unsupervised learning,AbundanceBin,F-measure,MCluster,MetaCluster 3.0,MetaCluster 5.0,N-grams,automatic feature weighting,basic K-means clustering algorithm,high-throughput technologies,metagenomic analysis,metagenomic data binning,metagenomic sequences,real data set,sampled microbial community,sensitivity,sequence feature extraction,short metagenomic reads,simulated data sets,taxonomical
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A New Unsupervised Binning Approach for Metagenomic Sequences Based on N-grams and Automatic Feature Weighting
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| contributor author | Ruiqi Liao | |
| contributor author | Ruichang Zhang | |
| contributor author | Jihong Guan | |
| contributor author | Shuigeng Zhou | |
| date accessioned | 2020-03-12T18:31:40Z | |
| date available | 2020-03-12T18:31:40Z | |
| date issued | 2014 | |
| identifier issn | 1545-5963 | |
| identifier other | 6654133.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/961461?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | A New Unsupervised Binning Approach for Metagenomic Sequences Based on N-grams and Automatic Feature Weighting | |
| type | Journal Paper | |
| contenttype | Metadata Only | |
| identifier padid | 7994263 | |
| subject keywords | biology computing | |
| subject keywords | feature extraction | |
| subject keywords | genomics | |
| subject keywords | microorganisms | |
| subject keywords | sensitivity | |
| subject keywords | sequences | |
| subject keywords | unsupervised learning | |
| subject keywords | AbundanceBin | |
| subject keywords | F-measure | |
| subject keywords | MCluster | |
| subject keywords | MetaCluster 3.0 | |
| subject keywords | MetaCluster 5.0 | |
| subject keywords | N-grams | |
| subject keywords | automatic feature weighting | |
| subject keywords | basic K-means clustering algorithm | |
| subject keywords | high-throughput technologies | |
| subject keywords | metagenomic analysis | |
| subject keywords | metagenomic data binning | |
| subject keywords | metagenomic sequences | |
| subject keywords | real data set | |
| subject keywords | sampled microbial community | |
| subject keywords | sensitivity | |
| subject keywords | sequence feature extraction | |
| subject keywords | short metagenomic reads | |
| subject keywords | simulated data sets | |
| subject keywords | taxonomical | |
| identifier doi | 10.1109/TCBB.2013.137 | |
| journal title | Computational Biology and Bioinformatics, IEEE/ACM Transactions on | |
| journal volume | 11 | |
| journal issue | 1 | |
| filesize | 2394154 | |
| citations | 0 |