An extensible deep learning framework for multi-type chromatin interaction prediction through hierarchical adaptation strategies
- DOI
- 10.1371/journal.pcbi.1014911
- Published
- 2026-10-27
- Container
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pcbi.1014911,
title = {An extensible deep learning framework for multi-type chromatin interaction prediction through hierarchical adaptation strategies},
author = {Shuaibin Wang and Tong Chen and Weixu Liu and Xuejie Zheng and Zhongxin Yang and Zhen Liang and Yin Shen},
year = {2026},
journal = {PLOS Computational Biology},
doi = {10.1371/journal.pcbi.1014911},
url = {https://doi.org/10.1371/journal.pcbi.1014911}
}RIS
TY - JOUR TI - An extensible deep learning framework for multi-type chromatin interaction prediction through hierarchical adaptation strategies AU - Shuaibin Wang AU - Tong Chen AU - Weixu Liu AU - Xuejie Zheng AU - Zhongxin Yang AU - Zhen Liang AU - Yin Shen PY - 2026 JO - PLOS Computational Biology DO - 10.1371/journal.pcbi.1014911 UR - https://doi.org/10.1371/journal.pcbi.1014911 ER -
APA
Wang, S., Chen, T., Liu, W., Zheng, X., Yang, Z., Liang, Z., & Shen, Y. (2026). An extensible deep learning framework for multi-type chromatin interaction prediction through hierarchical adaptation strategies. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1014911
Source records
- crossref · retrieved 2026-11-06T20:11:13.334Z