Discovering differential genome sequence activity with interpretable and efficient deep learning
<jats:p>Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods th...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135690 |