{"id":"W2321529004","doi":"10.1158/1538-7445.am2012-3004","title":"Abstract 3004: Biomarker identification through integrative bioinformatics analysis of serous epithelial ovarian cancer tumor samples","year":2012,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer, Lipids, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Ovarian cancer; Serous fluid; Cancer research; Biology; Gene expression profiling; Cancer; Biomarker; Gene signature; Oncology; Medicine; Bioinformatics; Gene expression; Internal medicine; Gene; 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":[0.0008949984,0.0004396394,0.0006940921,0.00188641,0.0003822296,0.0009511124,0.0003250613,0.0003632296,0.0007571487],"category_scores_gemma":[0.001361539,0.0001243473,0.000634062,0.001589332,0.0001913424,0.0002005287,0.0004968534,0.0003247354,0.0004242278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002490741,"about_ca_system_score_gemma":0.0006838011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169241,"about_ca_topic_score_gemma":0.001750369,"domain_scores_codex":[0.999386,0.0001270134,0.00006727846,0.0001844097,0.0001685194,0.0000667482],"domain_scores_gemma":[0.9994367,0.0002017562,0.0001079627,0.00005717058,0.0001535589,0.00004291674],"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":[0.003065872,0.001065666,0.3293207,0.001198838,0.0009015462,0.001532938,0.0004727791,0.01349089,0.4825899,0.0006069877,0.006150477,0.1596034],"study_design_scores_gemma":[0.0001601403,0.001402957,0.6878394,0.00009439625,0.0009935644,0.002934367,0.0008677281,0.1443447,0.1420507,0.001830586,0.01738209,0.00009926446],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9610239,0.001209835,0.01964876,0.0002852788,0.00003684594,0.0001763122,0.01607448,0.0007009138,0.0008435149],"genre_scores_gemma":[0.917958,0.0004898206,0.04260565,0.0001771821,0.00004145902,0.0003637778,0.03757432,0.00006045054,0.0007293755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00188641,"threshold_uncertainty_score":0.004733264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07670747037085987,"score_gpt":0.4034206894487259,"score_spread":0.3267132190778661,"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."}}