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Deep learning identifies and quantifies recombination hotspot determinants
View ORCID ProfileYu Li, View ORCID ProfileSiyuan Chen, Trisevgeni Rapakoulia, Hiroyuki Kuwahara, View ORCID ProfileKevin Y. Yip, Xin Gao
doi: https://doi.org/10.1101/2021.07.29.454133
Yu Li
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
2Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
3The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
Siyuan Chen
2Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Trisevgeni Rapakoulia
4Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
Hiroyuki Kuwahara
2Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Kevin Y. Yip
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
Xin Gao
2Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Posted July 29, 2021.
Deep learning identifies and quantifies recombination hotspot determinants
Yu Li, Siyuan Chen, Trisevgeni Rapakoulia, Hiroyuki Kuwahara, Kevin Y. Yip, Xin Gao
bioRxiv 2021.07.29.454133; doi: https://doi.org/10.1101/2021.07.29.454133
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