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Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks
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Peter K. Koo ,
Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
* E-mail: koo@cshl.edu
Affiliation Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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Antonio Majdandzic,
Roles Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing
Affiliation Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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Matthew Ploenzke,
Roles Methodology, Writing – original draft, Writing – review & editing
Affiliation Department of Biostatistics, Harvard University, Cambridge, Massachusetts, United States of America
⨯ - Praveen Anand,
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Steffan B. Paul
Roles Data curation, Formal analysis, Methodology, Validation, Visualization
Affiliation Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, Massachusetts, United States of America
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https://orcid.org/0000-0001-8722-0038
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks
- Peter K. Koo,
- Antonio Majdandzic,
- Matthew Ploenzke,
- Praveen Anand,
- Steffan B. Paul
- Published: May 13, 2021
- https://doi.org/10.1371/journal.pcbi.1008925
- See the preprint