ArticlePloS one2015
Evidence-based translation for the genomic responses of murine models for the study of human immunity.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- ctGAN: combined transformation of gene expression and survival data with generative adversarial network.Briefings in bioinformatics · 2024Article
- AutoTransOP: translating omics signatures without orthologue requirements using deep learning.NPJ systems biology and applications · 2024Article
- Comprehensive bioinformatics analysis reveals biomarkers of DNA methylation-related genes in varicose veins.Frontiers in genetics · 2022Article
- An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease.Science signaling · 2020Article
- Computational translation of genomic responses from experimental model systems to humans.PLoS computational biology · 2019Article
- Found In Translation: a machine learning model for mouse-to-human inference.Nature methods · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 evidence-based translation (EBT) to improve the analysis of genomic responses of murine models in the translation to human immune responses. Based on evidences from prior experiments, EBT 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, EBT significantly improves the agreement of overall responses (up to 56%), experiment-specific responses (up to 143%), and enriched biological contexts (up to 100%) between human and model systems. In addition, we provide the category of genes specifically benefiting from EBT and the factors affecting the performance of EBT. The overall result indicates the usefulness of the proposed computational translation in biomedical research for human immunity using murine models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.