{"id":"W1964276428","doi":"10.1517/17460441.2011.555394","title":"Small interfering ribonucleic acid design strategies for effective targeting and gene silencing","year":2011,"lang":"en","type":"article","venue":"Expert Opinion on Drug Discovery","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McGill University","funders":"","keywords":"Small interfering RNA; Gene silencing; RNA interference; Computational biology; Computer science; Biology; Gene; RNA; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000183742,0.0002189547,0.0001664898,0.00005278771,0.0001153394,0.00008762623,0.0001617477,0.00009407284,0.00000810853],"category_scores_gemma":[0.00004796766,0.0001963509,0.00009476425,0.00003268807,0.00005838998,0.00003531871,0.000114929,0.0000756372,0.000004101907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001865237,"about_ca_system_score_gemma":0.0000471264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005608132,"about_ca_topic_score_gemma":0.000007417889,"domain_scores_codex":[0.9989575,0.00007069178,0.0001926333,0.000453099,0.00006424917,0.0002618768],"domain_scores_gemma":[0.9995717,0.00003684726,0.00007767677,0.0002060493,0.00004849645,0.00005922492],"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":[0.0006003878,0.00005906849,0.00008227753,0.00002093508,0.00006577811,0.000001604555,0.001857191,0.0001035497,0.9903729,0.0001294767,0.001807107,0.004899731],"study_design_scores_gemma":[0.0003789898,0.0008668731,0.0002175389,0.00009013899,0.000003208326,0.000004993706,0.002376932,0.0003068053,0.9944115,0.0002093081,0.0008557254,0.0002779471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093746,0.004188391,0.0849615,0.00002901244,0.0006585249,0.0004216319,0.00001535807,0.00002301024,0.0003279769],"genre_scores_gemma":[0.9951584,0.001102381,0.002747855,0.0002699054,0.000383805,0.0001629312,0.00005485038,0.00003296826,0.00008686652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08578385,"threshold_uncertainty_score":0.8006958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03955378461515262,"score_gpt":0.2666056667078847,"score_spread":0.227051882092732,"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."}}