{"id":"W2262611560","doi":"10.1261/rna.055186.115","title":"A high-throughput pipeline for the production of synthetic antibodies for analysis of ribonucleoprotein complexes","year":2016,"lang":"en","type":"article","venue":"RNA","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; College of Family Physicians of Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Toronto","keywords":"Biology; Ribonucleoprotein; Immunoprecipitation; Computational biology; RNA; Antibody; Small nuclear ribonucleoprotein; Molecular biology; Phage display; Cell biology; Gene; Genetics","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":[0.0002641277,0.00005842894,0.000161377,0.00004915353,0.000051247,0.000003548733,0.0001111007,0.000033132,0.000006381924],"category_scores_gemma":[0.0004867058,0.00003115654,0.0001495874,0.00009760062,0.0001138458,0.000002247079,0.00003191716,0.00001251172,2.821226e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004355762,"about_ca_system_score_gemma":0.00001929904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007943958,"about_ca_topic_score_gemma":0.00009610083,"domain_scores_codex":[0.9994519,0.00001854819,0.0001643791,0.0001593977,0.00007874872,0.0001269751],"domain_scores_gemma":[0.9993574,0.0000774346,0.00009515083,0.0002668754,0.0001863196,0.00001682311],"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":[0.0002024006,0.0000288284,0.0002883911,0.00004834805,0.0003298943,2.722199e-8,0.00001209075,0.00006784395,0.9844635,0.0003054141,0.0008115952,0.01344171],"study_design_scores_gemma":[0.0002094617,0.0001729918,0.00143426,0.00002750589,0.0001837375,3.258757e-7,0.00003195773,0.0007597976,0.9952505,0.0001677892,0.001713792,0.0000478878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308929,0.0003690421,0.06697688,0.001189166,0.00004402253,0.0004043674,0.000111218,0.00000282561,0.000009576689],"genre_scores_gemma":[0.9967436,0.0001855754,0.001577544,0.00001095976,0.00009845477,0.00004552421,0.00002280351,0.000007558932,0.001308029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06585065,"threshold_uncertainty_score":0.1270527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02321979500265021,"score_gpt":0.3030940654394259,"score_spread":0.2798742704367757,"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."}}