Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture - researchr publication related

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Zutan Li, Jingya Fang, Shining Wang, Liangyun Zhang, Yuanyuan Chen, Cong Pian. Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture. Briefings in Bioinformatics, 23(2), 2022. [doi]

The following publications are possibly variants of this publication:

Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning methodHao Lv, Fu-Ying Dao, Zheng-Xing Guan, Hui Yang, Yan-Wen Li, Hao Lin 0001. bib, 22(4), 2021. [doi]

DTL-NeddSite: A Deep-Transfer Learning Architecture for Prediction of Lysine Neddylation SitesDeli Xu, Yafei Zhu, Qiang Xu, Yuhai Liu, Yu Chen, Yang Zou, Lei Li 0013. access, 11:51798-51809, 2023. [doi]

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