CCTAD: A topologically associating domains detection method integrating convolutional autoencoder and hierarchical clustering

Evaluation of CCTAD across multiple cell lines and resolutions demonstrates its advantages in boundary identification of key regulatory elements.

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Publication Details

Journal
Computational Biology and Chemistry
Published
2026-01-21
DOI
https://doi.org/10.1016/j.compbiolchem.2026.108913
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

CCTAD: A topologically associating domains detection method integrating convolutional autoencoder and hierarchical clustering

Feng RuiPing, Luo Junwei, Liu Kaihua, Guo Fei
Computational Biology and Chemistry
Cell Image Analysis Techniques
article

CCTAD: A topologically associating domains detection method integrating convolutional autoencoder and hierarchical clustering

Feng RuiPing, Luo Junwei, Liu Kaihua, Guo Fei
article en

Abstract

Evaluation of CCTAD across multiple cell lines and resolutions demonstrates its advantages in boundary identification of key regulatory elements.

Computational Biology and ChemistryVol. 122
Central South University (CN), Henan Polytechnic University (CN)
Openalex Percentile: Top 84%
Cell Image Analysis Techniques
DOIOpen Access PDFOpenAlex
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CCTAD: A topologically associating domains detection method integrating convolutional autoencoder and hierarchical clustering — Feng RuiPing, Luo Junwei, et al. · Computational Biology and Chemistry (2026) | TGRS Research Map | TGRS