BioOrbitX: An Explainable Cross-Tissue Framework for Prioritizing Spaceflight-Responsive Genes

Spaceflight perturbs physiology across organs, yet candidate-gene prioritization is commonly tissue-specific or based solely on differential-expression (DE) statistics. This study examines liver (OSD-168; 31,428 genes; n=5 ground-control and n=5 spaceflight animals in the selected RR-1 contrast) and soleus muscle (OSD-104; 22,437 genes; n=6 and n=6) RNA-seq contrasts. Genes with Benjamini–Hochberg-adjusted p1 were designated DE responders. Random forest, XGBoost, L1-logistic regression, and a soft-voting ensemble were evaluated within tissue and transferred between tissues using six expression/topology descriptors. Single-split AUROC/F1 estimates reached 0.934/0.443 in liver and 0.978/0.534 in muscle, whereas transfer was weaker (best AUROC 0.799, liver-to-muscle). SHAP ranking identified tissue-specific candidates, including Smad3, Myorg, Arrdc3, Bdh1, Fzd9, Mettl21c and Nqo1 in muscle; the top-50 sets did not overlap. An implementation audit showed that the original column filter retained source-table summary/statistic fields alongside per-animal columns; therefore, the reported model scores are exploratory and not a count-only, leakage-free validation. The contribution is a fully specified candidate-prioritization workflow and an explicit reproducibility boundary for the required count-only, repeated-resampling rerun.