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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
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  • For correspondence: liyu@cse.cuhk.edu.hk xin.gao@kaust.edu.sa
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
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Trisevgeni Rapakoulia
4Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
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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
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Kevin Y. Yip
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
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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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  • For correspondence: liyu@cse.cuhk.edu.hk xin.gao@kaust.edu.sa
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Article Information

doi 
https://doi.org/10.1101/2021.07.29.454133
History 
  • July 29, 2021.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Yu Li1,2,3,*,#,
  2. Siyuan Chen2,#,
  3. Trisevgeni Rapakoulia4,
  4. Hiroyuki Kuwahara2,
  5. Kevin Y. Yip1 and
  6. Xin Gao2,*
  1. 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
  2. 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
  3. 3The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
  4. 4Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
  1. ↵*Correspondence to: liyu{at}cse.cuhk.edu.hk (Y.L.) and xin.gao{at}kaust.edu.sa (X.G.).
  1. ↵# These authors contributed equally to this work.

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Posted July 29, 2021.
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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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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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