{"id":"W2084674294","doi":"10.1021/pr0703223","title":"A Proteome Resource of Ovarian Cancer Ascites: Integrated Proteomic and Bioinformatic Analyses To Identify Putative Biomarkers","year":2007,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"U.S. Department of Defense","keywords":"Proteome; Ovarian cancer; Proteomics; Biomarker discovery; Biomarker; Computational biology; Ascites; Human proteome project; Biology; Cancer biomarkers; Cancer; Bioinformatics; Identification (biology); Medicine; 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":[0.0006420191,0.0006530599,0.0006977139,0.001717958,0.0006729634,0.001017904,0.0004228698,0.0002959078,0.0008441902],"category_scores_gemma":[0.001363125,0.000192026,0.0003653247,0.002041701,0.000132484,0.0007551012,0.0006887304,0.0003982763,0.0005565197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000383732,"about_ca_system_score_gemma":0.0008697233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001049536,"about_ca_topic_score_gemma":0.001546314,"domain_scores_codex":[0.9997012,0.0000878734,0.00002839729,0.00006253381,0.00008881846,0.00003123291],"domain_scores_gemma":[0.999584,0.0001121903,0.00004578261,0.00005676294,0.0001250932,0.00007618616],"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":[0.001837384,0.0006084253,0.02370593,0.001015921,0.0002804325,0.0013601,0.0004204109,0.01007227,0.8118321,0.002113586,0.008615862,0.1381375],"study_design_scores_gemma":[0.0003347717,0.00117012,0.1408768,0.0001955457,0.0005706277,0.003169128,0.0008166913,0.3009996,0.4721388,0.007888812,0.0716305,0.0002085247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8700493,0.002159246,0.08785573,0.0007202557,0.00005374073,0.000458271,0.0322598,0.003758908,0.002684781],"genre_scores_gemma":[0.6907178,0.001227198,0.225078,0.0001164377,0.00004736077,0.0003850611,0.08113976,0.0002326912,0.001055731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001717958,"threshold_uncertainty_score":0.003395319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09701701893645145,"score_gpt":0.48758793679221,"score_spread":0.3905709178557586,"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."}}