CGLoop: a neural network framework for chromatin loop prediction
The experimental results show that, loops predicted by CGLoop show high APA scores and there is an enrichment of multiple transcription factors and binding proteins at the predicted loops anchors, which outperforms other methods in terms of accuracy and validity of chromatin loops prediction.
Authors
- Lili Wu (ORCID: https://orcid.org/0000-0003-0680-8716)
- Huimin Luo (ORCID: https://orcid.org/0000-0003-1840-3716)
- Junfeng Wang (ORCID: https://orcid.org/0000-0003-1699-2270)
- Junwei Luo (ORCID: https://orcid.org/0000-0001-6841-9893)
- Jingjing Wei (ORCID: https://orcid.org/0000-0003-2221-6702)
- Chaokun Yan (ORCID: https://orcid.org/0000-0002-6246-7242)
- Fei Guo (ORCID: https://orcid.org/0000-0003-2911-7643)
Institutions
- Central South University (CN)
- Henan University (CN)
- Anyang Institute of Technology (CN)
- Henan Polytechnic University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1186/s12864-025-11531-y
- Citations
- 3
- Primary Topic
- Genomics and Chromatin Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 2.42
Funders
- Henan Polytechnic University