New Results
Identifying common transcriptome signatures of cancer by interpreting deep learning models
View ORCID ProfileAnupama Jha, View ORCID ProfileMathieu Quesnel-Vallières, View ORCID ProfileAndrei Thomas-Tikhonenko, Kristen W. Lynch, View ORCID ProfileYoseph Barash
doi: https://doi.org/10.1101/2021.11.11.467790
Anupama Jha
1Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Mathieu Quesnel-Vallières
2Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
3Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
Andrei Thomas-Tikhonenko
4Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
5Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
6Division of Cancer Pathobiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
Kristen W. Lynch
2Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
3Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
Yoseph Barash
1Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
3Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA

- Supplementary tables[supplements/467790_file03.xlsx]
Posted November 12, 2021.
Identifying common transcriptome signatures of cancer by interpreting deep learning models
Anupama Jha, Mathieu Quesnel-Vallières, Andrei Thomas-Tikhonenko, Kristen W. Lynch, Yoseph Barash
bioRxiv 2021.11.11.467790; doi: https://doi.org/10.1101/2021.11.11.467790
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