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Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach
Hongzhu Cui, Chong Zhou, Xinyu Dai, Yuting Liang, Randy Paffenroth, View ORCID ProfileDmitry Korkin
doi: https://doi.org/10.1101/214122
Hongzhu Cui
1Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
Chong Zhou
2Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
Xinyu Dai
2Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
Yuting Liang
2Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
Randy Paffenroth
2Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
3Mathematics Department, Worcester Polytechnic Institute, Worcester, MA, USA 010609
4Computer Science Department, Worcester Polytechnic Institute, Worcester, MA, USA 010609
Dmitry Korkin
1Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
2Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA 010609
4Computer Science Department, Worcester Polytechnic Institute, Worcester, MA, USA 010609
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Posted November 05, 2017.
Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach
Hongzhu Cui, Chong Zhou, Xinyu Dai, Yuting Liang, Randy Paffenroth, Dmitry Korkin
bioRxiv 214122; doi: https://doi.org/10.1101/214122
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