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Predicting Antibody Developability from Sequence using Machine Learning
Xingyao Chen, Thomas Dougherty, Chan Hong, Rachel Schibler, Yi Cong Zhao, Reza Sadeghi, Naim Matasci, Yi-Chieh Wu, Ian Kerman
doi: https://doi.org/10.1101/2020.06.18.159798
Xingyao Chen
1Department of Computer Science, Harvey Mudd College, Claremont, California, USA
Thomas Dougherty
1Department of Computer Science, Harvey Mudd College, Claremont, California, USA
Chan Hong
1Department of Computer Science, Harvey Mudd College, Claremont, California, USA
Rachel Schibler
1Department of Computer Science, Harvey Mudd College, Claremont, California, USA
Yi Cong Zhao
1Department of Computer Science, Harvey Mudd College, Claremont, California, USA
Reza Sadeghi
2Dassault Systèmes BIOVIA, San Diego, California, USA
Naim Matasci
3Department of Biology, Harvey Mudd College, Claremont, California, USA
4Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
Yi-Chieh Wu
1Department of Computer Science, Harvey Mudd College, Claremont, California, USA
Ian Kerman
2Dassault Systèmes BIOVIA, San Diego, California, USA
Article usage
Posted June 20, 2020.
Predicting Antibody Developability from Sequence using Machine Learning
Xingyao Chen, Thomas Dougherty, Chan Hong, Rachel Schibler, Yi Cong Zhao, Reza Sadeghi, Naim Matasci, Yi-Chieh Wu, Ian Kerman
bioRxiv 2020.06.18.159798; doi: https://doi.org/10.1101/2020.06.18.159798
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