CoNet app: inference of biological association networks using Cytoscape
Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes,...
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ftunivleuven:oai:lirias.kuleuven.be:123456789/557520 2023-05-15T15:00:18+02:00 CoNet app: inference of biological association networks using Cytoscape Faust, Karoline Raes, Jeroen 2016-10 https://lirias.kuleuven.be/handle/123456789/557520 http://f1000research.com/articles/10.12688/f1000research.9050.1/doi https://lirias.kuleuven.be/bitstream/123456789/557520/4//2016223.pdf en eng Faculty of 1000 Ltd. F1000Research vol:5 pages:1519 https://lirias.kuleuven.be/handle/123456789/557520 2046-1402 http://f1000research.com/articles/10.12688/f1000research.9050.1/doi https://lirias.kuleuven.be/bitstream/123456789/557520/4//2016223.pdf 425344;public Article IT 425344;Article 2016 ftunivleuven 2017-06-02T19:41:55Z Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet. status: published Article in Journal/Newspaper Arctic KU Leuven: Lirias Arctic |
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KU Leuven: Lirias |
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ftunivleuven |
language |
English |
description |
Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet. status: published |
format |
Article in Journal/Newspaper |
author |
Faust, Karoline Raes, Jeroen |
spellingShingle |
Faust, Karoline Raes, Jeroen CoNet app: inference of biological association networks using Cytoscape |
author_facet |
Faust, Karoline Raes, Jeroen |
author_sort |
Faust, Karoline |
title |
CoNet app: inference of biological association networks using Cytoscape |
title_short |
CoNet app: inference of biological association networks using Cytoscape |
title_full |
CoNet app: inference of biological association networks using Cytoscape |
title_fullStr |
CoNet app: inference of biological association networks using Cytoscape |
title_full_unstemmed |
CoNet app: inference of biological association networks using Cytoscape |
title_sort |
conet app: inference of biological association networks using cytoscape |
publisher |
Faculty of 1000 Ltd. |
publishDate |
2016 |
url |
https://lirias.kuleuven.be/handle/123456789/557520 http://f1000research.com/articles/10.12688/f1000research.9050.1/doi https://lirias.kuleuven.be/bitstream/123456789/557520/4//2016223.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
F1000Research vol:5 pages:1519 https://lirias.kuleuven.be/handle/123456789/557520 2046-1402 http://f1000research.com/articles/10.12688/f1000research.9050.1/doi https://lirias.kuleuven.be/bitstream/123456789/557520/4//2016223.pdf |
op_rights |
425344;public |
_version_ |
1766332410087079936 |