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DeepANIS: Predicting antibody paratope from concatenated CDR sequences by integrating bidirectional long-short-term memory and transformer neural networks
Pan Zhang, View ORCID ProfileShuangjia Zheng, View ORCID ProfileJianwen Chen, Yaoqi Zhou, View ORCID ProfileYuedong Yang
doi: https://doi.org/10.1101/2021.08.16.456569
Pan Zhang
1School of Computer Science and Engineering, Sun Yat-sen University, GHuangzhou 510000, China
Shuangjia Zheng
1School of Computer Science and Engineering, Sun Yat-sen University, GHuangzhou 510000, China
2Galixir, Beijing, China
Jianwen Chen
1School of Computer Science and Engineering, Sun Yat-sen University, GHuangzhou 510000, China
Yaoqi Zhou
3Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
4Peking University Shenzhen Graduate School, Shenzhen, 518055, P.R.China
Yuedong Yang
1School of Computer Science and Engineering, Sun Yat-sen University, GHuangzhou 510000, China
Posted August 17, 2021.
DeepANIS: Predicting antibody paratope from concatenated CDR sequences by integrating bidirectional long-short-term memory and transformer neural networks
Pan Zhang, Shuangjia Zheng, Jianwen Chen, Yaoqi Zhou, Yuedong Yang
bioRxiv 2021.08.16.456569; doi: https://doi.org/10.1101/2021.08.16.456569
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