Evidence map›Paper›PMID 37200521›Full record

ArticleChemical research in toxicology2023

TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes.

Ting Li, Ruth Roberts, Zhichao Liu, Weida Tong

Abstract read
In one paragraph

Article in Chemical research in toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.Toxicological sciences : an official journal of the Society of Toxicology · 2026
    Article
  3. Article
  4. Review
  5. Review
  6. AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  7. Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  8. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ting LiNational Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States.ORCID 0000-0003-2280-8433
Ruth RobertsApconiX Ltd, Alderley Park, Alderley Edge SK10 4TG, United Kingdom.ORCID 0000-0002-7763-7558
Zhichao LiuIntegrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut 06877, United States.
Weida TongNational Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States.ORCID 0000-0002-4093-3708

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Animal studies are required for the evaluation of candidate drugs to ensure patient and volunteer safety. Toxicogenomics is often applied in these studies to gain understanding of the underlying mechanisms of toxicity, which is usually focused on critical organs such as the liver or kidney in young male rats. There is a strong ethical reason to reduce, refine and replace animal use (the 3Rs), where the mapping of data between organs, sexes and ages could reduce the cost and time of drug development. Herein, we proposed a generative adversarial network (GAN)-based framework entitled TransOrGAN that allowed the molecular mapping of gene expression profiles in different rodent organ systems and across sex and age groups. We carried out a proof-of-concept study based on rat RNA-seq data from 288 samples in 9 different organs of both sexes and 4 developmental stages. First, we demonstrated that TransOrGAN could infer transcriptomic profiles between any 2 of the 9 organs studied, yielding an average cosine similarity of 0.984 between synthetic transcriptomic profiles and their corresponding real profiles. Second, we found that TransOrGAN could infer transcriptomic profiles observed in females from males, with an average cosine similarity of 0.984. Third, we found that TransOrGAN could infer transcriptomic profiles in juvenile, adult, and aged animals from adolescent animals with an average cosine similarity of 0.981, 0.983, and 0.989, respectively. Altogether, TransOrGAN is an innovative approach to infer transcriptomic profiles between ages, sexes, and organ systems, offering the opportunity to reduce animal usage and to provide an integrated assessment of toxicity in the whole organism irrespective of sex or age.

Indexed as

Artificial IntelligenceTranscriptomeAnimalsFemaleMaleRats

Identifiers

PMID37200521
PMCPMC10433534

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Registered trials

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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.