Connecting the Dots: Leveraging AI/ML Tools to Increase Interpretability of Genomic Signatures from Mouse to Human

Music City Center 

Mouse models have long been used to characterize human diseases and drug signatures. However, due to differences between organisms, translating genomic changes can be quite challenging. Therefore, understanding the linkages between human and mouse remains crucial.
In our study, we utilized PBMC mouse and human single-cell RNA-seq time course data under trauma conditions to conduct transfer learning analysis. We carefully examined the characteristics of genes with good translatability and explored their linkage to 'causal' genes. Collectively, our work aims to enhance the understanding of disease mechanisms and drug actions based on animal models.

AI/ML, translation, mouse model