mEBT: multiple-matching Evidence-based Translator

EBTS

mEBT The multiple-matching Evidence-based Translator. Murine models are an essential tool to study human immune responses and related diseases. However, the use of traditional murine models has been challenged by recent systemic surveys that show discordance between human and model immune responses in their gene expression. This is a significant problem in translational biomedical research for human immunity. Here, we describe multitple-matching evidence-based translator (mEBT) to improve the analysis of genomic responses of murine models in the translation to human immune responses. Based on evidences from prior experiments, mEBT introduces pseudo variances, penalizes gene expression changes in a model experiment, and finally detects false positive translations of model genomic responses that poorly correlate with human responses. Demonstrated over multiple data sets, mEBT significantly improves the agreement of overall responses between human and model systems.
Seok J (2015) Evidence-Based Translation for the Genomic Responses of Murine Models for the Study of Human Immunity. PLoS ONE 10(2): e0118017. doi:10.1371/journal.pone.0118017
Tae D & Seok J (2018) mEBT: multiple-matching Evidence-based Translator between Murine and Humand Genomic Responses (in prepration)

The outline of the mEBT