Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
NeutralArtificial Intelligence
This article discusses gene regulatory network inference, focusing on how genes regulate each other based on gene expression data. It highlights the challenges posed by selection bias and latent confounders, such as non-coding RNAs, which can create misleading statistical dependencies. Various methods have been developed to tackle these issues in the field.
— Curated by the World Pulse Now AI Editorial System
