New Results
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
Matthew Ploenzke
2Department of Biostatistics, Harvard University, Boston, MA, USA
Praveen Anand
3Dana-Farber Cancer Institute, Boston, MA, USA
Steffan B. Paul
4Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
Antonio Majdandzic
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

Article usage
Posted September 09, 2020.
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
Subject Area
Subject Areas
- Biochemistry
- Biochemistry (14178)
- Bioengineering (10827)
- Bioinformatics (34318)
- Biophysics (17656)
- Cancer Biology (14758)
- Cell Biology (20784)
- Clinical Trials (138)
- Developmental Biology (11184)
- Ecology (16504)
- Epidemiology (2067)
- Evolutionary Biology (20813)
- Genetics (13677)
- Genomics (19101)
- Immunology (14246)
- Microbiology (33167)
- Molecular Biology (13838)
- Neuroscience (72412)
- Paleontology (542)
- Pathology (2278)
- Pharmacology and Toxicology (3860)
- Physiology (6102)
- Plant Biology (12391)
- Synthetic Biology (3461)
- Systems Biology (8371)
- Zoology (1913)