ArticleFrontiers in genetics2026
Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Understanding tissue-specific transcriptomic structures in livestock is essential for elucidating the molecular basis of economically important traits. Conventional differential gene expression analysis efficiently identifies genes with large average expression differences but does not fully capture multivariate expression structures and gene-gene interaction patterns that define tissue identity. In this study, we developed an explainable machine learning framework to classify seven Hanwoo cattle tissues using RNA sequencing data and to systematically compare the relative contributions of statistical and model-derived signals. A Random Forest-based one-versus-rest classification model was trained on 130 Hanwoo transcriptomes and externally validated using 231 independent
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