{"id":"W2900482380","doi":"10.1038/s41592-018-0218-5","title":"Interpretation of an individual functional genomics experiment guided by massive public data","year":2018,"lang":"en","type":"article","venue":"Nature Methods","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health; Nature; National Institute of General Medical Sciences; Canadian Institute for Advanced Research","keywords":"Context (archaeology); Functional genomics; Genomics; Data science; Data integration; Interpretation (philosophy); Divergence (linguistics); Computational biology; Computer science; Biology; Genome; Genetics; Data mining; Gene","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":[0.0009328418,0.0001177749,0.000125916,0.00003833731,0.0000579459,0.00003488716,0.0004763267,0.0003785815,0.00005184522],"category_scores_gemma":[0.0001297213,0.0001065107,0.00003764429,0.0000708197,0.0001097377,0.00001510042,0.0003641986,0.0001803195,0.000002076682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001542905,"about_ca_system_score_gemma":0.00009413759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002646434,"about_ca_topic_score_gemma":0.000005430145,"domain_scores_codex":[0.999031,0.0001478642,0.0002638763,0.0002764525,0.0001245868,0.0001562313],"domain_scores_gemma":[0.9989215,0.00001989432,0.0001693363,0.0006605849,0.0001567636,0.00007198086],"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":[0.000141078,0.00009244784,0.00004349368,0.00001342977,0.0002382993,2.219949e-7,0.0003452509,0.00003027704,0.7062152,0.0006909186,0.08933996,0.2028494],"study_design_scores_gemma":[0.0007187899,0.0005245873,0.0002793643,0.000008096922,0.00005072457,0.00001413279,0.000310911,0.02876484,0.6520366,0.00106613,0.3159233,0.0003025078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1289754,0.003292961,0.8621541,0.0003039854,0.001336433,0.0002689741,0.0004040895,0.00001525742,0.003248786],"genre_scores_gemma":[0.6252158,0.00003105011,0.3690352,0.0007616094,0.0005988233,0.000007769847,0.004176528,0.00001742338,0.0001557668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4962403,"threshold_uncertainty_score":0.4343379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04151593774553253,"score_gpt":0.3749987887243116,"score_spread":0.3334828509787791,"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."}}