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.

Guardado en:
Detalles Bibliográficos
Autores principales: C. F. Blum, N. Heramvand, A. S. Khonsari, M. Kollmann
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
Materias:
Q
Acceso en línea:https://doaj.org/article/48ea6446771949eeae6ac10c57fbbdff
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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.