{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007300218,0.0005020711,0.0004819434,0.002335731,0.0006016051,0.000879537,0.0003866702,0.000592327,0.001556327],"category_scores_gemma":[0.0007598624,0.0003734523,0.0006145425,0.001111412,0.0003351069,0.0009146233,0.0007692548,0.000676611,0.0005791666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005651321,"about_ca_system_score_gemma":0.0004708671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007395952,"about_ca_topic_score_gemma":0.001559522,"domain_scores_codex":[0.9995216,0.00006128439,0.00002389198,0.0001788581,0.0001501398,0.00006416715],"domain_scores_gemma":[0.9994828,0.0002275357,0.0001026625,0.00005832749,0.00007164053,0.00005706928],"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":[0.000297395,0.00003487196,0.005589318,0.0003170252,0.00008955856,0.0002452818,0.0001377852,0.001371162,0.9718062,0.001497537,0.0005546906,0.01805922],"study_design_scores_gemma":[0.00006673285,0.0002387658,0.08732855,0.00004575516,0.0001838954,0.001348304,0.0003784844,0.1057679,0.7779499,0.008320712,0.01829815,0.00007286941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8377384,0.002452029,0.1495413,0.000284268,0.00004203158,0.0001400154,0.005110682,0.001511686,0.003179556],"genre_scores_gemma":[0.8105296,0.001704329,0.1733869,0.0003298114,0.00006442021,0.0002719651,0.01091981,0.0004569194,0.002336269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002335731,"threshold_uncertainty_score":0.005206466,"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."}}