{"id":"W4414183210","doi":"10.1038/s41592-025-02795-z","title":"Spatial gene expression at single-cell resolution from histology using deep learning with GHIST","year":2025,"lang":"en","type":"article","venue":"Nature Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Innovation and Technology Commission; University of Sydney; University of Guelph","keywords":"Deep learning; In silico; Benchmarking; Transcriptome; Spatial analysis; Flexibility (engineering); Scalability; Genomics; Genome","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002847839,0.0002317226,0.0002577855,0.00008136168,0.0002645096,0.00002147756,0.0001930827,0.0008150607,0.00002841537],"category_scores_gemma":[0.0001549517,0.0002056241,0.00009800573,0.0001331173,0.0001085568,0.000004567877,0.0001227337,0.0005502107,0.000001404893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001308407,"about_ca_system_score_gemma":0.0000668949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001829525,"about_ca_topic_score_gemma":0.0002209057,"domain_scores_codex":[0.9982617,0.0005324957,0.0002274273,0.0005704921,0.0001250409,0.0002828066],"domain_scores_gemma":[0.9992794,0.00007598934,0.0001294443,0.0003459681,0.0001041131,0.00006507976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000664748,0.00007782828,0.004643857,0.00001907334,0.00002807201,0.00000609392,0.00005658728,0.0006928493,0.9838055,0.000006932198,0.0001390611,0.009859337],"study_design_scores_gemma":[0.00089883,0.0001907478,0.000667198,0.00003553017,0.00007648614,0.000009063468,0.00002042918,0.002653509,0.941393,0.0000515558,0.05377449,0.0002292031],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4382817,0.006566306,0.5528867,0.00003861737,0.0006536976,0.0001008969,0.000005883088,0.00002513894,0.001441011],"genre_scores_gemma":[0.6002924,0.00004843159,0.3979584,0.0002476846,0.0002460235,0.000005481962,0.0002006349,0.00002855134,0.0009722978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1620107,"threshold_uncertainty_score":0.8385108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399649049669491,"score_gpt":0.2925983031975287,"score_spread":0.2786018127008338,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}