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bioRxiv · 10.1101/2025.02.18.638832

TISSLET Tissues-based Learning Estimation for Transcriptomics

Miloudi, A.·Al-Qahtani, A.·Hashir, T.·Chikri, M.·Bensmail, H.
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Abstract

In the context of multi-omics data analytics for various diseases, transcriptome-wide association studies leveraging genetically predicted gene expression hold promise for identifying novel regions linked to complex traits. However, existing methods for multi-tissue gene expression prediction often fail to account for tissue-tissue expression interactions, limiting their accuracy and effectiveness. This research addresses the challenge of predicting gene expression across multiple tissues by incorporating tissue-tissue expression correlations based on a nonlinear multivariate model. Our findings demonstrate that this model excels in estimating tissue-tissue interactions and accurately predicting missing data. These results have significant implications for multi-omics data analytics and transcriptome-wide association studies, suggesting a novel approach for identifying regions associated with complex traits.

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Miloudi, A., Al-Qahtani, A., Hashir, T., Chikri, M., Bensmail, H.. 2025-02-23. TISSLET Tissues-based Learning Estimation for Transcriptomics. https://doi.org/10.1101/2025.02.18.638832

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