{"id":"W2101975193","doi":"10.1261/rna.047803.114","title":"Dissecting noncoding and pathogen RNA–protein interactomes","year":2014,"lang":"en","type":"article","venue":"RNA","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health; National Human Genome Research Institute; International Life Sciences Institute Research Foundation; Howard Hughes Medical Institute","keywords":"Biology; Interactome; Computational biology; RNA; RNA-binding protein; Immunoprecipitation; Non-coding RNA; Protein–protein interaction; Genome; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001961204,0.00007046171,0.00007200943,0.00002439971,0.00007783469,0.00003874157,0.00008106553,0.00004689467,0.00000979512],"category_scores_gemma":[0.0002732918,0.00006120761,0.00002969711,0.00003123214,0.00002252542,0.00000338132,0.00009826457,0.0000725877,0.000006617653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005259182,"about_ca_system_score_gemma":0.000008943305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002853895,"about_ca_topic_score_gemma":0.00001847515,"domain_scores_codex":[0.9994568,0.00002895775,0.00008283679,0.0001765646,0.00007000851,0.000184874],"domain_scores_gemma":[0.9997033,0.00001392739,0.00002804236,0.0001391214,0.00003450086,0.00008114289],"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":[0.000016466,0.000005332778,0.0020512,0.00001319269,0.000009083444,0.000001853819,0.00002282247,0.000001540876,0.9794097,0.0000806867,0.00006212849,0.01832597],"study_design_scores_gemma":[0.0002413214,0.0001836473,0.003220812,0.00004805191,0.00000341755,0.000009411995,0.0001124944,0.0007292148,0.9870568,0.0001109174,0.008139884,0.0001440321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946173,0.0002050376,0.00112287,0.00008346733,0.00004483138,0.00006505385,0.000001884848,0.000008174973,0.00385139],"genre_scores_gemma":[0.9967435,0.00002999068,0.0004355574,0.0000614313,0.000254016,0.000005299718,0.00001044539,0.00001149046,0.002448287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01818194,"threshold_uncertainty_score":0.2495974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008959995639962647,"score_gpt":0.2707657010398024,"score_spread":0.2618057053998397,"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."}}