Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks is a research paper published in PLoS Computational Biology (2021). On theSindex it has a DataRank of 0. It has been cited 97 times.
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NCI NIH HHS
Grant: P30 CA045508
NCI NIH HHS
Grant: T32 CA009337
FWCI
5.41
Citation Percentile
1.0%
Citation Trend
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Keywords
Additional file 1 of EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations
Additional file 1 of EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations
Additional file 2 of EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations
Additional file 2 of EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations
Additional file 1 of Correcting gradient-based interpretations of deep neural networks for genomics
Additional file 1 of Correcting gradient-based interpretations of deep neural networks for genomics
Additional file 2 of Correcting gradient-based interpretations of deep neural networks for genomics
Additional file 2 of Correcting gradient-based interpretations of deep neural networks for genomics