frankchen121212/RHSNet: Tensorflow and Keras implementation of RHSNet for recombination hotspot identification and quantification
dc.contributor.author | Li, Yu | |
dc.contributor.author | Chen, Siyuan | |
dc.contributor.author | Rapakoulia, Trisevgeni | |
dc.contributor.author | Kuwahara, Hiroyuki | |
dc.contributor.author | Yip, Kevin Y | |
dc.contributor.author | Gao, Xin | |
dc.date.accessioned | 2022-05-09T06:22:41Z | |
dc.date.available | 2022-05-09T06:22:41Z | |
dc.date.issued | 2021-05-14 | |
dc.identifier.uri | http://hdl.handle.net/10754/676686 | |
dc.description.abstract | Tensorflow and Keras implementation of RHSNet for recombination hotspot identification and quantification | |
dc.publisher | Github | |
dc.relation.url | https://github.com/frankchen121212/RHSNet | |
dc.title | frankchen121212/RHSNet: Tensorflow and Keras implementation of RHSNet for recombination hotspot identification and quantification | |
dc.type | Software | |
dc.contributor.department | Computational Bioscience Research Center (CBRC) | |
dc.contributor.department | Computer Science | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division | |
dc.contributor.department | Structural and Functional Bioinformatics Group | |
dc.contributor.institution | Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China | |
dc.contributor.institution | The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China | |
dc.contributor.institution | Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany | |
kaust.person | Li, Yu | |
kaust.person | Chen, Siyuan | |
kaust.person | Kuwahara, Hiroyuki | |
kaust.person | Gao, Xin | |
dc.relation.issupplementto | DOI:10.1093/bioinformatics/btac234 | |
display.relations | <b>Is Supplement To:</b><br/> <ul><li><i>[Article]</i> <br/> Li, Y., Chen, S., Rapakoulia, T., Kuwahara, H., Yip, K. Y., & Gao, X. (2022). Deep learning identifies and quantifies recombination hotspot determinants. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac234. DOI: <a href="https://doi.org/10.1093/bioinformatics/btac234" >10.1093/bioinformatics/btac234</a> Handle: <a href="http://hdl.handle.net/10754/676242" >10754/676242</a></a></li></ul> | |
dc.identifier.github | frankchen121212/RHSNet |
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Structural and Functional Bioinformatics Group
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Computer Science Program
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Computational Bioscience Research Center (CBRC)
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Software
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/