The following publications are possibly variants of this publication:
- A Review About Transcription Factor Binding Sites Prediction Based on Deep LearningYuanqi Zeng, Meiqin Gong, Meng Lin, Dongrui Gao, Yongqing Zhang. access, 8:219256-219274, 2020. [doi]
- High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning methodYongqing Zhang, Zixuan Wang, Yuanqi Zeng, Jiliu Zhou, Quan Zou 0001. bib, 22(6), 2021. [doi]
- DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networksChen Chen, Jie Hou, Xiaowen Shi, Hua Yang, James A. Birchler, Jianlin Cheng. bmcbi, 22(1):38, 2021. [doi]
- ML-Consensus: A General Consensus Model for Variable-Length Transcription Factor Binding SitesSaad Quader, Nathan Snyder, Kevin Su, Ericka Mochan, Chun-Hsi Huang. evoW 2011: 25-36 [doi]
- maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networksTareian A. Cazares, Faiz W. Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Anthony T. Bejjani, Joseph A. Wayman, Omer Donmez, Benjamin Wronowski, Sreeja Parameswaran, Leah C. Kottyan, Artem Barski, Matthew T. Weirauch, V. B. Surya Prasath, Emily R. Miraldi. ploscb, 19(1), January 2023. [doi]
- An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep LearningFang Jing, Shao-Wu Zhang 0001, Zhen Cao, Shihua Zhang. tcbb, 18(1):355-364, 2021. [doi]
- DeepBSI: a multimodal deep learning framework for predicting the transcription factor binding site and intensityPeng Zhang, Shikui Tu. bibm 2021: 785-788 [doi]