{"id":"W2261457272","doi":"","title":"MALDI imaging mass spectrometry in ovarian cancer for tracking, identifying, and validating biomarkers.","year":2010,"lang":"en","type":"article","venue":"PubMed","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Proteomics; Ovarian cancer; Biology; Mass spectrometry imaging; Biomarker discovery; Proteome; Immune system; MALDI imaging; Cancer research; Computational biology; Mass spectrometry; Cancer; Chemistry; Bioinformatics; Immunology; Biochemistry; Matrix-assisted laser desorption/ionization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003966482,0.0001505318,0.0001685246,0.0001869409,0.0001043522,0.0001589415,0.0002134554,0.00009043832,0.0008465016],"category_scores_gemma":[0.0001123946,0.0001622417,0.00005939448,0.0003066066,0.00005060803,0.0001475357,0.00005356146,0.0002872773,7.141225e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000750714,"about_ca_system_score_gemma":0.00001486824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002309927,"about_ca_topic_score_gemma":0.0001068503,"domain_scores_codex":[0.9987395,0.000006082236,0.0002623495,0.0003944808,0.0001199312,0.0004775898],"domain_scores_gemma":[0.9993653,0.00007777259,0.0001290079,0.0002884073,0.00003253198,0.0001069231],"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":[0.00001559394,0.00006139957,0.2253717,0.0002038451,0.00004078496,0.000005333534,0.00005581246,1.964328e-7,0.6574074,0.008953213,0.0004731832,0.1074115],"study_design_scores_gemma":[0.001082463,0.00000330275,0.2580473,0.00003673821,0.00005543726,0.00002650418,0.0001519809,0.0005414396,0.7072976,0.02049393,0.01174641,0.0005169026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9327686,0.000485432,0.008990885,0.002818495,0.0002530749,0.001237637,0.0001481185,0.0004121427,0.05288558],"genre_scores_gemma":[0.9774939,0.00003896273,0.01793593,0.00005130381,0.0002114632,0.003752969,0.0000182855,0.00003595026,0.00046118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1068946,"threshold_uncertainty_score":0.9268599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207965079079217,"score_gpt":0.2823210124945211,"score_spread":0.260241361703729,"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."}}