{"id":"W4406110088","doi":"10.1101/2025.01.05.631419","title":"DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"","keywords":"Interactivity; Computer science; Flexibility (engineering); Data science; Data mining; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002202295,0.000455861,0.0007847708,0.0004216606,0.00005412251,0.00005220931,0.0003524727,0.0005687052,0.000006998025],"category_scores_gemma":[0.00007186364,0.0005264814,0.0002466451,0.000293583,0.0001459144,0.000008734952,0.0002069509,0.0002874724,7.066429e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008482869,"about_ca_system_score_gemma":0.0001781968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004025482,"about_ca_topic_score_gemma":0.0003015109,"domain_scores_codex":[0.9979209,0.00008975195,0.0005449189,0.0009476769,0.0001426126,0.0003541769],"domain_scores_gemma":[0.9987499,0.00003891421,0.0002914578,0.0006574139,0.0001489742,0.0001133055],"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":[0.0003292985,0.0002197403,0.02230462,0.0003827164,0.001298859,0.00000368805,0.00004627718,0.0002866546,0.9750406,0.00002300109,0.00003632577,0.00002827952],"study_design_scores_gemma":[0.001049117,0.0001747762,0.055878,0.0001890232,0.001143333,7.391098e-9,0.000008422306,0.001434888,0.9352236,0.000001491116,0.004388407,0.0005089158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710032,0.001856351,0.02468396,0.00007031969,0.0004242825,0.000827443,0.001103893,0.00002143322,0.000009148698],"genre_scores_gemma":[0.9936046,0.001127577,0.004865455,0.00007109476,0.0001069546,0.0001363755,0.00001255553,0.00005511857,0.00002028145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03981692,"threshold_uncertainty_score":0.9997187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009802191521859547,"score_gpt":0.2371332190441621,"score_spread":0.2273310275223025,"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."}}