{"id":"W1829370794","doi":"10.1093/database/bav059","title":"G4RNA: an RNA G-quadruplex database","year":2015,"lang":"it","type":"article","venue":"Database","topic":"DNA and Nucleic Acid Chemistry","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; G-quadruplex; Information retrieval; Sequence (biology); Computational biology; Theoretical computer science; Data mining; Biology; DNA; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001188532,0.00121669,0.001454732,0.006107795,0.0007738564,0.002253431,0.003119136,0.002127348,0.01511295],"category_scores_gemma":[0.004750975,0.0006189358,0.0006938103,0.005813251,0.0003793413,0.002159591,0.002317602,0.0009704091,0.02099449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006221626,"about_ca_system_score_gemma":0.001969935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454785,"about_ca_topic_score_gemma":0.001867304,"domain_scores_codex":[0.9992728,0.0001022782,0.0001977562,0.0001856782,0.0001735753,0.00006792996],"domain_scores_gemma":[0.9985979,0.0003932696,0.000246858,0.00024718,0.0002534436,0.0002613313],"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":[0.003316016,0.0006136356,0.01003104,0.01426906,0.0004112532,0.002098284,0.0007016502,0.007431303,0.1004216,0.01855902,0.5192515,0.3228957],"study_design_scores_gemma":[0.0005073411,0.0004554794,0.009991849,0.0005894491,0.0002605012,0.001488215,0.0003901378,0.0154395,0.04442503,0.01412066,0.912093,0.0002388265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02030166,0.003855874,0.06356202,0.0005318934,0.0001818004,0.0008217364,0.8332697,0.06359156,0.01388371],"genre_scores_gemma":[0.0235789,0.001574647,0.07059161,0.000384664,0.00004579664,0.0006242617,0.8979555,0.001987455,0.003257145],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01511295,"threshold_uncertainty_score":0.05055779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03413014463313407,"score_gpt":0.2888167434363996,"score_spread":0.2546865988032656,"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."}}