A new unsupervised binning approachfor metagenomic sequences based onN-grams and automatic feature weighting

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 11, Issue 1, 2014, Pages 42-54

  Liao, Ruiqi ^a   Zhang, Ruichang ^a   Guan, Jihong ^b   Zhou, Shuigeng ^a  

^a FUDAN UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

Author keywords

algorithms; binning; feature weighting; Metagenomics; N grams

Indexed keywords

ALGORITHMS; GENES;

BINNING; FEATURE WEIGHTING; HIGH-THROUGHPUT TECHNOLOGIES; K-MEANS CLUSTERING ALGORITHM; LEARNING-BASED APPROACH; METAGENOMICS; MICROBIAL COMMUNITIES; N-GRAMS;

FLUORINE;

ALGORITHM; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENETICS; HIGH THROUGHPUT SEQUENCING; METAGENOMICS; MICROBIAL CONSORTIUM; PROCEDURES;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTER SIMULATION; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; METAGENOMICS; MICROBIAL CONSORTIA;

EID: 84901367862     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2013.137     Document Type: Article

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