Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.

ABSTRACT: Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar G

SUBMITTER: Park Y 

PROVIDER: S-EPMC10835749 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature