Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach
Abstract Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01317-z |
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| _version_ | 1850061999597158400 |
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| author | Ting Li Xi Chen Weida Tong |
| author_facet | Ting Li Xi Chen Weida Tong |
| author_sort | Ting Li |
| collection | DOAJ |
| description | Abstract Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI model using a generative adversarial network (GAN) method to facilitate the bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment. TransTox demonstrates robust performance, validated across independent datasets and laboratories. First, the concordance between real experimental data and synthetic data generated by TransTox was demonstrated in characterizing toxicity mechanisms compared to real experimental settings. Second, TransTox proved valuable in gene expression predictive models, where synthetic data could be used to develop gene expression predictive models or serve as “digital twins” for diagnostic applications. The TransTox approach holds the potential for multi-organ toxicity assessment with AI and advancing the field of precision toxicology. |
| format | Article |
| id | doaj-art-e8442d37e7a64726937da9ce5ed5d6ec |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-e8442d37e7a64726937da9ce5ed5d6ec2025-08-20T02:50:02ZengNature Portfolionpj Digital Medicine2398-63522024-11-017111410.1038/s41746-024-01317-zBridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approachTing Li0Xi Chen1Weida Tong2FDA National Center for Toxicological ResearchFDA National Center for Toxicological ResearchFDA National Center for Toxicological ResearchAbstract Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI model using a generative adversarial network (GAN) method to facilitate the bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment. TransTox demonstrates robust performance, validated across independent datasets and laboratories. First, the concordance between real experimental data and synthetic data generated by TransTox was demonstrated in characterizing toxicity mechanisms compared to real experimental settings. Second, TransTox proved valuable in gene expression predictive models, where synthetic data could be used to develop gene expression predictive models or serve as “digital twins” for diagnostic applications. The TransTox approach holds the potential for multi-organ toxicity assessment with AI and advancing the field of precision toxicology.https://doi.org/10.1038/s41746-024-01317-z |
| spellingShingle | Ting Li Xi Chen Weida Tong Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach npj Digital Medicine |
| title | Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach |
| title_full | Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach |
| title_fullStr | Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach |
| title_full_unstemmed | Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach |
| title_short | Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach |
| title_sort | bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative ai approach |
| url | https://doi.org/10.1038/s41746-024-01317-z |
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