{"id":"W4412163796","doi":"10.1158/1557-3265.aimachine-a006","title":"Abstract A006: Data Curation and Knowledge Integration Pipeline for Biomarker Discovery","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Data curation; Pipeline (software); Data science; Biomarker; Medicine; Data integration; Biomarker discovery; Computational biology; Computer science; Biology; Data mining; Proteomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004807425,0.004133721,0.002170403,0.008657182,0.00147396,0.005244555,0.003765478,0.00159618,0.0633984],"category_scores_gemma":[0.01369296,0.001675293,0.00351138,0.005816394,0.0006122643,0.003422295,0.005252362,0.00307748,0.05156378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180919,"about_ca_system_score_gemma":0.005347229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007462585,"about_ca_topic_score_gemma":0.007836832,"domain_scores_codex":[0.9972146,0.0004377783,0.0004350493,0.0008854286,0.0008689419,0.0001581092],"domain_scores_gemma":[0.9944038,0.001906247,0.0003505245,0.001336809,0.001551981,0.0004506102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001253332,0.0004657638,0.004435375,0.004140853,0.0007474146,0.0007477604,0.0004954059,0.00667609,0.02149399,0.007289386,0.688295,0.2639597],"study_design_scores_gemma":[0.001217493,0.0005140263,0.01231524,0.0009038053,0.0005854022,0.0009429725,0.0005181322,0.1927419,0.05026222,0.06123265,0.678268,0.0004982303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.002118568,0.0005430286,0.3675006,0.001055304,0.0002883798,0.00168585,0.1845939,0.4371974,0.005016972],"genre_scores_gemma":[0.01520754,0.0006755039,0.5888225,0.0009571126,0.0001350463,0.00265441,0.3681667,0.01806524,0.005315819],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0633984,"threshold_uncertainty_score":0.2120888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4185490787243473,"score_gpt":0.6062314006747581,"score_spread":0.1876823219504108,"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."}}