{"id":"W4394717749","doi":"10.1186/s13041-024-01088-4","title":"The 37TrillionCells initiative for improving global healthcare via cell-based interception and precision medicine: focus on neurodegenerative diseases","year":2024,"lang":"en","type":"article","venue":"Molecular Brain","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; McGill University Health Centre; Université de Montréal; Montreal Clinical Research Institute","funders":"Fonds de Recherche du Québec - Santé; Fondation du Grand défi Pierre Lavoie; Institut de recherche, Centre universitaire de santé McGill; Fondation de l'Hôpital de Montréal pour enfants; Compute Canada; Canadian Institutes of Health Research; Djavad Mowafaghian Foundation","keywords":"Precision medicine; Disease; Computer science; Medicine; Neuroscience; Bioinformatics; Data science; Psychology; Biology; Pathology","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":[0.006048004,0.00225398,0.001040517,0.001405941,0.001128613,0.003996738,0.002073176,0.003276042,0.01461669],"category_scores_gemma":[0.001618593,0.0003350287,0.001166348,0.001172793,0.001903196,0.002902897,0.005567797,0.003932636,0.008858748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002276883,"about_ca_system_score_gemma":0.008869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002669226,"about_ca_topic_score_gemma":0.003034557,"domain_scores_codex":[0.9975432,0.0004665041,0.00005820237,0.0003879633,0.0009089166,0.0006352924],"domain_scores_gemma":[0.9970719,0.0003974354,0.000206866,0.0002584321,0.0005938179,0.001471655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001258695,0.000501162,0.00196571,0.001381825,0.0001460863,0.0008348874,0.0004562314,0.002591529,0.1643043,0.1284081,0.3864551,0.3116964],"study_design_scores_gemma":[0.0001858043,0.0007112461,0.001031989,0.0002191761,0.00006446027,0.0003704719,0.0001466964,0.001949287,0.06231077,0.008615198,0.9243438,0.00005121015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05647452,0.1552862,0.245747,0.1327524,0.03211033,0.002836076,0.01887255,0.02284644,0.3330745],"genre_scores_gemma":[0.1774908,0.1321243,0.389322,0.05047668,0.006132851,0.003191314,0.03668447,0.004042536,0.200535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01461669,"threshold_uncertainty_score":0.04889774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175124808612396,"score_gpt":0.2731302211975714,"score_spread":0.2613789731114474,"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."}}