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Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks

View ORCID ProfilePeter K. Koo, Matthew Ploenzke, Praveen Anand, Steffan B. Paul, Antonio Majdandzic
doi: https://doi.org/10.1101/2020.09.08.288068
Peter K. Koo
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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  • ORCID record for Peter K. Koo
  • For correspondence: koo@cshl.edu
Matthew Ploenzke
2Department of Biostatistics, Harvard University, Boston, MA, USA
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Praveen Anand
3Dana-Farber Cancer Institute, Boston, MA, USA
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Steffan B. Paul
4Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
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Antonio Majdandzic
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Posted September 09, 2020.
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Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks
Peter K. Koo, Matthew Ploenzke, Praveen Anand, Steffan B. Paul, Antonio Majdandzic
bioRxiv 2020.09.08.288068; doi: https://doi.org/10.1101/2020.09.08.288068
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Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks
Peter K. Koo, Matthew Ploenzke, Praveen Anand, Steffan B. Paul, Antonio Majdandzic
bioRxiv 2020.09.08.288068; doi: https://doi.org/10.1101/2020.09.08.288068

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