{"id":"W2011287359","doi":"10.1038/nmeth.1424","title":"Computational prediction of neural progenitor cell fates","year":2010,"lang":"en","type":"article","venue":"Nature Methods","topic":"Retinal Development and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":114,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université de Montréal; Montreal Clinical Research Institute","funders":"Canadian Institutes of Health Research","keywords":"Progenitor cell; Biology; Stem cell; Cell fate determination; Computational biology; Cell division; Computer science; Progenitor; Identification (biology); Cell biology; Cell; Genetics; Gene","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":[0.0009629017,0.0006265506,0.001204912,0.0009348896,0.0006163565,0.001388615,0.002239256,0.001694583,0.004136187],"category_scores_gemma":[0.005172513,0.001024776,0.001088673,0.0007532347,0.0007432647,0.001087941,0.0008745439,0.001475019,0.0005486424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009234208,"about_ca_system_score_gemma":0.001571448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063239,"about_ca_topic_score_gemma":0.01081839,"domain_scores_codex":[0.9997869,0.0000697931,0.00001400824,0.00005087827,0.00004561408,0.00003273542],"domain_scores_gemma":[0.9964046,0.003020816,0.0001025732,0.0001579618,0.0002050116,0.0001089251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007170621,0.00003176056,0.0009550677,0.000027542,0.00002647739,0.0000480797,0.00001008998,0.9864997,0.0002161488,0.003169613,0.0006321715,0.008311731],"study_design_scores_gemma":[0.000005421795,0.000002146863,0.0000273655,8.315487e-7,0.000001954383,0.0000026543,0.000001143567,0.9988061,0.0000609628,0.001062789,0.00002793605,7.684411e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5278882,0.0009018135,0.455397,0.002048162,0.0002761596,0.000106836,0.001734081,0.002970617,0.008677147],"genre_scores_gemma":[0.9393051,0.0001689329,0.05693105,0.0001932012,0.0000772887,0.0001385005,0.001067943,0.0001395115,0.001978444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01063239,"threshold_uncertainty_score":0.02114099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006090576339896629,"score_gpt":0.3155458795675906,"score_spread":0.309455303227694,"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."}}