Iterative Clustering Method for Metagenomic Sequences

Bonet, Isis; Montoya, Widerman; Mesa-Múnera, Andrea; Alzate, Juan F.

doi:10.1007/978-3-319-13817-6_15

Cited by 5 publications

(8 citation statements)

References 10 publications

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“…It is important to remember that high purity is easy to achieve when the number of clusters is large [20]. This method provided positive results generating less clusters that are equally pure.…”

Section: Clustering Methods Based On Combine Different Distancesmentioning

confidence: 97%

“…To assess the final quality of clustering methods we use a labeled database, intra and inter-cluster, and purity measure (4) [20].…”

Section: Clustering Methodsmentioning

confidence: 99%

“…We have proposed a clustering method based on iterative clustering [20] with kmean++ as the base method. The main idea behind this proposal is the usage of multiple distances, in order to make different divisions of space input.…”

Section: Clustering Methods Based On Combine Different Distancesmentioning

confidence: 99%

“…Taking into account some previous results [20], we select k-mer (k=4) as the features to represent the contigs.…”

Section: Featuresmentioning

confidence: 99%

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Clustering of Metagenomic Data by Combining Different Distance Functions

2017

APH

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Section: Clustering Methods Based On Combine Different Distancesmentioning

confidence: 97%

“…To assess the final quality of clustering methods we use a labeled database, intra and inter-cluster, and purity measure (4) [20].…”

Section: Clustering Methodsmentioning

confidence: 99%

Section: Clustering Methods Based On Combine Different Distancesmentioning

confidence: 99%

“…Taking into account some previous results [20], we select k-mer (k=4) as the features to represent the contigs.…”

Section: Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Clustering of Metagenomic Data by Combining Different Distance Functions

2017

APH

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“… k-mers (k=4), which is a 4-combination of the nucleotides. That means words of 4 letters [13]. The 4-mers are computed as the number of each tetranucleotide and normalized considering the total of tetranucleotides in the contig.…”

Section: Data and Featuresmentioning

confidence: 99%

Recursive Clustering Using Different Features Sets for Metagenomic Data

Bonet¹

2018

JCP

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Metagenomics binning process is a step prior to the taxonomic assignment of metagenomic reads of contigs, which helps to group genome sequences belonging to the same species. In this paper we propose a clustering method that is executed recursively to cluster contigs into groups of same taxa. In each step the method increases the taxonomic level, beginning with a domain and ending with a group that represents the species. The method uses a previous rule-based system to separate virus from the rest of the organism and feature selection algorithms to select different features in each step of the clustering. The clustering is based on k-means++ using Cosine and Jaccard distance, and feature selection on gain information. The proposed method outperforms classic k-means++, achieving 88.15% of purity in clusters.

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Clustering Algorithm Optimization Applied to Metagenomics Using Big Data

Vanegas

Bonet

2018

Advances in Intelligent Systems and Computing

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Iterative Clustering Method for Metagenomic Sequences

Cited by 5 publications

References 10 publications

Clustering of Metagenomic Data by Combining Different Distance Functions

Clustering of Metagenomic Data by Combining Different Distance Functions

Recursive Clustering Using Different Features Sets for Metagenomic Data

Clustering Algorithm Optimization Applied to Metagenomics Using Big Data

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