{"id":"W4225378098","doi":"10.1101/2022.05.02.490100","title":"A resource for generating and manipulating human microglial states in vitro","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neuroinflammation and Neurodegeneration Mechanisms","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Stanley Center for Psychiatric Research, Broad Institute; National Institutes of Health; Kuopion Yliopistollinen Sairaala; Broad Institute; Life Sciences Research Foundation; Open Philanthropy Project; Alzheimer's Association; Howard Hughes Medical Institute","keywords":"Microglia; Biology; Neuroscience; Transcription factor; Neuroinflammation; Cell type; Transcriptional regulation; Computational biology; Cell; Cell biology; Gene; Immunology; Inflammation; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009759534,0.0007388195,0.0004611937,0.0008130303,0.0004922417,0.0006117725,0.0007131715,0.000385407,0.002411518],"category_scores_gemma":[0.0003763065,0.0003482693,0.0003728955,0.0003659847,0.0003275481,0.0003217996,0.0008349156,0.0006641573,0.002688998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003062045,"about_ca_system_score_gemma":0.0003577488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005343469,"about_ca_topic_score_gemma":0.001255291,"domain_scores_codex":[0.9994857,0.0001355333,0.00005435741,0.00008756513,0.0001918929,0.00004481774],"domain_scores_gemma":[0.9997368,0.00007637788,0.00003700885,0.00009721352,0.00002884758,0.00002365296],"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":[0.0000486467,0.00005287161,0.0001989716,0.00007853093,0.000007704274,0.0001292314,0.00005594562,0.0008703112,0.9903611,0.0008902735,0.0008318835,0.006474655],"study_design_scores_gemma":[0.00001515236,0.0001173785,0.0005731475,0.00002766296,0.00002290009,0.0003460387,0.00002486917,0.002459678,0.9548935,0.0003637683,0.04113718,0.00001867414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1934792,0.003610013,0.7602431,0.0005005426,0.0002397972,0.001453542,0.01833383,0.007534734,0.01460516],"genre_scores_gemma":[0.4151616,0.005842817,0.5212057,0.0003566103,0.0001108238,0.004470335,0.03640049,0.001952474,0.01449919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002411518,"threshold_uncertainty_score":0.00806731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0347274823881539,"score_gpt":0.2543962360113488,"score_spread":0.2196687536231949,"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."}}