{"id":"W2065830704","doi":"10.1158/1538-7445.am2011-1168","title":"Abstract 1168: Integrated genomic, microRNA (miRNA) and proteomic profiling by stable isotope labeling with amino acids in cell culture (SILAC) of ovarian carcinoma for biomarker discovery","year":2011,"lang":"en","type":"article","venue":"Cancer Research","topic":"Coagulation, Bradykinin, Polyphosphates, and Angioedema","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Toronto General Hospital; University Health Network; University of Toronto; SickKids Foundation; Mount Sinai Hospital","funders":"","keywords":"microRNA; Stable isotope labeling by amino acids in cell culture; Biology; Biomarker; Cancer research; Ovarian cancer; Gene silencing; Gene expression profiling; Proteomics; Gene expression; Gene; Computational biology; Cancer; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003967189,0.0004380638,0.0004308603,0.0005179708,0.0003154051,0.0005365445,0.0002510115,0.0003352731,0.001313057],"category_scores_gemma":[0.0003022812,0.0001982458,0.0003328368,0.0005252576,0.0001941076,0.0002434839,0.0002667122,0.0005939357,0.001344535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568651,"about_ca_system_score_gemma":0.000402032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008108747,"about_ca_topic_score_gemma":0.001306908,"domain_scores_codex":[0.9996761,0.00003551953,0.00002853895,0.0000598913,0.0001691819,0.00003082907],"domain_scores_gemma":[0.9998415,0.00002872165,0.00002838108,0.00002690461,0.00005575909,0.00001871907],"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":[0.00003831995,0.0000100602,0.0002544419,0.00002409739,0.000003622513,0.0000173172,0.000008807237,0.00004014706,0.9982475,0.00003299558,0.00005170745,0.001271017],"study_design_scores_gemma":[0.000008678121,0.0001232236,0.004161962,0.000006556902,0.00002224419,0.000191657,0.0000195363,0.002709983,0.9877001,0.00004020043,0.005007621,0.00000814363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8955983,0.00734575,0.07936823,0.0003455105,0.0006042859,0.0004292595,0.006961306,0.001024903,0.008322586],"genre_scores_gemma":[0.8422437,0.005236756,0.1212994,0.000290026,0.00009206003,0.0006258291,0.01426583,0.0003027807,0.01564354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001313057,"threshold_uncertainty_score":0.004392624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06398212003329445,"score_gpt":0.3235935168182553,"score_spread":0.2596113967849609,"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."}}