{"id":"W1983815263","doi":"10.1089/cmb.2006.13.267","title":"RNA–RNA Interaction Prediction and Antisense RNA Target Search","year":2006,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Simon Fraser University","funders":"","keywords":"RNA; Non-coding RNA; Computational biology; Antisense RNA; Biology; Nucleic acid secondary structure; Gene; Nucleic acid structure; Algorithm; Sense (electronics); Genetics; Computer science; Chemistry","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":[0.001025978,0.0007703633,0.001133496,0.001207343,0.0004139527,0.0005528637,0.001576526,0.001494551,0.002587979],"category_scores_gemma":[0.002145981,0.0005843588,0.0007611832,0.001095724,0.0006132442,0.001145255,0.0006616388,0.0006860213,0.0007507434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004526473,"about_ca_system_score_gemma":0.0007208586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001599337,"about_ca_topic_score_gemma":0.001933736,"domain_scores_codex":[0.9994674,0.0002124863,0.00002307532,0.0001200386,0.0001214873,0.00005540393],"domain_scores_gemma":[0.9989121,0.0007957123,0.00009391358,0.00005432606,0.0001072961,0.00003661505],"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":[0.0002053995,0.0001635498,0.002249448,0.0001108981,0.00007321838,0.0001924364,0.00004566225,0.8603212,0.009141792,0.01178779,0.002116896,0.1135917],"study_design_scores_gemma":[0.00001351076,0.00002138812,0.0001473537,0.000002005309,0.00000662606,0.00003099874,0.000007570735,0.9928458,0.001793705,0.004810871,0.0003161043,0.00000399491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06532829,0.0002587981,0.9305492,0.000197198,0.00001384432,0.00005373189,0.000126337,0.001640619,0.001831942],"genre_scores_gemma":[0.4202184,0.0002024455,0.5750962,0.0001637279,0.00003420801,0.0002089773,0.0007369054,0.0002135329,0.003125594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002587979,"threshold_uncertainty_score":0.008657634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063543045560723,"score_gpt":0.267135800277262,"score_spread":0.2565003698216548,"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."}}