{"id":"W2262046392","doi":"","title":"MALDI MSI for ovarian cancer biomarkers research: latest developments of the technology for screening and tracking.","year":2012,"lang":"en","type":"article","venue":"ORBi (University of Liège)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Cancer Research; Institut National Du Cancer; Agence Nationale de la Recherche; Fonds de Recherche du Québec - Santé; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Ovarian cancer; Computational biology; Medicine; Internal medicine; Oncology; Biology; Cancer research; Cancer; Bioinformatics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003337567,0.001782146,0.001159133,0.002475925,0.0004169891,0.001848344,0.001110762,0.001827438,0.003582913],"category_scores_gemma":[0.002006337,0.0006500137,0.0005048763,0.001509847,0.0007986831,0.001600974,0.001369659,0.002374887,0.003550449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005776695,"about_ca_system_score_gemma":0.0008254093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009781981,"about_ca_topic_score_gemma":0.001660806,"domain_scores_codex":[0.9984027,0.0003496885,0.00005893794,0.0002962155,0.0007925036,0.0001000246],"domain_scores_gemma":[0.9984811,0.0005070451,0.0001642467,0.0000974151,0.0005687586,0.0001814716],"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":[0.000368945,0.0001211065,0.002556641,0.00160021,0.0001588557,0.0002144531,0.0001047684,0.000312505,0.5830538,0.001892879,0.01199212,0.3976237],"study_design_scores_gemma":[0.00009441538,0.001073933,0.01172965,0.0004598541,0.0002863444,0.005410637,0.0001949609,0.008020652,0.6801615,0.003508822,0.2888128,0.0002465514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.02473653,0.8021802,0.1478693,0.00930858,0.002329051,0.0002547765,0.001008102,0.002101175,0.01021221],"genre_scores_gemma":[0.1437424,0.5615895,0.2634557,0.004658569,0.003831834,0.0004231149,0.002077314,0.0004124751,0.01980897],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003582913,"threshold_uncertainty_score":0.0176509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05357255245533363,"score_gpt":0.3168473192232121,"score_spread":0.2632747667678785,"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."}}