{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003250316,0.00009114866,0.0001402788,0.00009788419,0.00006667505,0.00001943951,0.00007276082,0.0001219538,0.00002657395],"category_scores_gemma":[0.00004340995,0.0000782165,0.00007054514,0.00004547011,0.0000593667,0.0000102924,0.00003147464,0.0001040494,0.000003891726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001401754,"about_ca_system_score_gemma":0.00006314056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009796647,"about_ca_topic_score_gemma":0.000001066432,"domain_scores_codex":[0.9991693,0.0001448859,0.0003217807,0.0001355271,0.0001081091,0.0001204147],"domain_scores_gemma":[0.999411,0.00005404408,0.0002079845,0.00005672895,0.0002224538,0.00004778713],"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":[0.000198655,0.00004742823,0.00122506,0.000006582387,0.00005461361,0.000007004904,0.00001353517,0.004620896,0.9822676,0.0007702861,0.001447094,0.009341205],"study_design_scores_gemma":[0.00122203,0.001590748,0.01571576,0.00003726967,0.00003211452,0.0008528724,0.00005886985,0.002041449,0.9317905,0.02444805,0.02200463,0.0002057604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9310924,0.0008320587,0.06668175,0.0004267985,0.0003960785,0.0000680805,0.00002051118,0.000004220487,0.0004781116],"genre_scores_gemma":[0.9881085,0.0001319293,0.01077101,0.0001036216,0.000719953,0.000001423532,0.00007391807,0.000008596531,0.00008105474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05701611,"threshold_uncertainty_score":0.3189576,"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."}}