{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000872043,0.0008965403,0.0003353402,0.001303735,0.000293412,0.0005769341,0.000523901,0.001063291,0.001279719],"category_scores_gemma":[0.0006846785,0.0002843552,0.000322693,0.0006740515,0.0003886161,0.0006276022,0.000553074,0.0009502984,0.001080996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004236016,"about_ca_system_score_gemma":0.0004308991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003905765,"about_ca_topic_score_gemma":0.00056313,"domain_scores_codex":[0.9995988,0.00009430791,0.00001891993,0.00008486291,0.0001732951,0.00002985171],"domain_scores_gemma":[0.9997073,0.00006827274,0.00007588511,0.00002322451,0.00008384246,0.00004142262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009017176,0.00003715678,0.001669791,0.000325581,0.00003259687,0.0001670577,0.00003855765,0.0002392776,0.9622172,0.0006861404,0.0008695924,0.03362702],"study_design_scores_gemma":[0.00003860558,0.0003593887,0.01464121,0.00009809308,0.00008517965,0.003949626,0.000117439,0.0152224,0.9364877,0.002130767,0.02682055,0.00004896557],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3982624,0.1215147,0.4563533,0.004135531,0.000981146,0.0005862078,0.001916999,0.002730752,0.01351907],"genre_scores_gemma":[0.5573031,0.03323454,0.3949887,0.001463665,0.0003523875,0.0004750493,0.001489121,0.000176871,0.0105166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001303735,"threshold_uncertainty_score":0.00461185,"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."}}