Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm.
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| Main Authors: | , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2018
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| Subjects: | |
| Online Access: | https://doaj.org/article/48ea6446771949eeae6ac10c57fbbdff |
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| Summary: | Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm. |
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