(Q45943880)

18 December 2017

title

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data. (English)

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18 December 2017

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Oluwatosin Oluwadare

1

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18 December 2017

Jianlin Cheng

series ordinal

2

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18 December 2017

publication date

14 November 2017

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18 December 2017

published in

BMC Bioinformatics

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18 December 2017

volume

18

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18 December 2017

issue

1

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18 December 2017

page(s)

480

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18 December 2017

https://scigraph.springernature.com/pub.10.1186/s12859-017-1931-2

exact match

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cites work

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24 May 2018

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24 May 2018

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Cohesin-dependent globules and heterochromatin shape 3D genome architecture in S. pombe

24 May 2018

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24 May 2018

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Insulator function and topological domain border strength scale with architectural protein occupancy

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24 May 2018

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24 May 2018

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Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions

24 May 2018

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24 May 2018

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Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions.

24 May 2018

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24 May 2018

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Capturing chromosome conformation

24 May 2018

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24 May 2018

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Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture.

24 May 2018

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A cluster separation measure.

15 August 2018

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A Cluster Separation Measure

15 August 2018

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Crossref

https://api.crossref.org/works/10.1186%2FS12859-017-1931-2

Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

21 January 2018

1 reference

Crossref

https://api.crossref.org/works/10.1186%2FS12859-017-1931-2

10.1186/S12859-017-1931-2

21 January 2018

Identifiers

DOI

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18 December 2017

Dimensions Publication ID

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1 reference

18 December 2017

PubMed publication ID

29137603

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18 December 2017