{"id":"W4229715815","doi":"10.1038/npre.2011.5162.2","title":"Principles for the post-GWAS functional characterisation of risk loci","year":2011,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Diamantina Institute, University of Queensland; Illawarra Health and Medical Research Institute; Tampereen Yliopisto; Karolinska Institutet; Università degli Studi di Trento; University of Dundee; Queen Mary University of London; University of Queensland; Universitat Pompeu Fabra; National Institutes of Health; Queen's University; Royal Marsden NHS Foundation Trust; Wellcome Trust; Dartmouth College; University of Wollongong; Child and Family Research Institute; University of Texas at Austin","keywords":"Genome-wide association study; Computational biology; Single-nucleotide polymorphism; Computer science; Biology; Genetics; Gene; Genotype","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006195334,0.0002373545,0.00021703,0.00004549118,0.000109565,0.00002650816,0.000409714,0.001235242,0.00007221297],"category_scores_gemma":[0.0008758791,0.000172705,0.0002836435,0.00003115381,0.00005950217,0.000003483593,0.0003633701,0.0005429503,0.000002761668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001406111,"about_ca_system_score_gemma":0.0001023315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002660429,"about_ca_topic_score_gemma":0.00001547213,"domain_scores_codex":[0.9988351,0.00005398276,0.000299177,0.0004503847,0.0001852609,0.0001760848],"domain_scores_gemma":[0.9983939,0.00009361171,0.000586714,0.0004690812,0.0004170893,0.00003962548],"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":[0.0008319665,0.0000545467,0.0008132592,0.0001962008,0.0003694039,1.335286e-7,0.0003567938,0.00005584818,0.9790489,0.002398778,0.001053959,0.01482022],"study_design_scores_gemma":[0.0002432072,0.0001435175,0.01492077,0.00006220442,0.0001711126,0.000003700684,0.00004172743,0.00004753955,0.9419843,0.002898411,0.03923136,0.0002521797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594265,0.00579866,0.02663188,0.0006221475,0.002962328,0.002017302,0.0008823605,0.0000393051,0.001619541],"genre_scores_gemma":[0.9928101,0.0007937458,0.003513837,0.0001650102,0.001063042,0.0002810848,0.0008461817,0.00004025822,0.0004867584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0381774,"threshold_uncertainty_score":0.9527315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119673261889259,"score_gpt":0.2471273036016139,"score_spread":0.2259305709827213,"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."}}