{"id":"W4405539283","doi":"10.1371/journal.pbio.3002965","title":"Lessons about physiological relevance learned from large-scale meta-analysis of co-expression networks in brain organoids","year":2024,"lang":"en","type":"letter","venue":"PLoS Biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Fonds de Recherche du Québec - Santé","keywords":"Biology; Organoid; Computational biology; Fidelity; Scale (ratio); Relevance (law); Data science; Human brain; Neuroscience; Bioinformatics; Computer science; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02433383,0.0007239287,0.001453672,0.0007704712,0.001249857,0.003241323,0.002147334,0.007519071,0.002290259],"category_scores_gemma":[0.07675835,0.0005865121,0.001415557,0.0008884345,0.003670546,0.003976509,0.002306502,0.01926543,0.002038434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354098,"about_ca_system_score_gemma":0.001487284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847149,"about_ca_topic_score_gemma":0.003256176,"domain_scores_codex":[0.9937483,0.002653529,0.0005498437,0.001666906,0.001129878,0.0002515309],"domain_scores_gemma":[0.9059725,0.07906887,0.001803387,0.006547323,0.004878434,0.001729466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.0008831155,0.00009410812,0.01426223,0.001233007,0.001526052,0.002995467,0.001219074,0.003204675,0.009365303,0.04007319,0.7184033,0.2067406],"study_design_scores_gemma":[0.0004276667,0.0002035374,0.01411604,0.0009533443,0.0005956178,0.002601955,0.0007407531,0.01214004,0.005351861,0.5214721,0.4410892,0.0003078495],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.005304442,0.01280155,0.02412374,0.9281092,0.0259078,0.00003071186,0.001210968,0.0004759912,0.002035692],"genre_scores_gemma":[0.1095894,0.01905127,0.02622656,0.7572854,0.08030725,0.000240998,0.001066954,0.0008976692,0.005334452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02433383,"threshold_uncertainty_score":0.1286911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07746093360570029,"score_gpt":0.3077354540216667,"score_spread":0.2302745204159664,"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."}}