{"id":"W2620890793","doi":"10.1016/j.ebiom.2017.06.001","title":"Combined Mass Spectrometry Imaging and Top-down Microproteomics Reveals Evidence of a Hidden Proteome in Ovarian Cancer","year":2017,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Direction Générale de l’offre de Soins; Institut National Du Cancer; Ministère de l'Enseignement Supérieur et de la Recherche; Institut Universitaire de France; Canada Research Chairs; Institut National de la Santé et de la Recherche Médicale","keywords":"Proteome; ORFS; Proteomics; Serous fluid; Western blot; Computational biology; Biology; Ovarian cancer; Mass spectrometry; Ovarian tumor; Molecular biology; Open reading frame; Cancer; Chemistry; Bioinformatics; Peptide sequence; Biochemistry; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002875876,0.000171777,0.0003577798,0.0001281811,0.0001371279,0.00003332572,0.0004403736,0.00008247841,0.0002037373],"category_scores_gemma":[0.000178708,0.0001589964,0.00003456344,0.000142075,0.0003182455,0.0001681264,0.0001651682,0.0002648454,0.000001362284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001119446,"about_ca_system_score_gemma":0.00005058015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006190705,"about_ca_topic_score_gemma":0.00001054237,"domain_scores_codex":[0.9988137,0.000008565169,0.000397605,0.0003591844,0.0001569573,0.0002640066],"domain_scores_gemma":[0.9985868,0.00004851221,0.000412039,0.0007951822,0.0000683131,0.00008916313],"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":[0.00006992667,0.0000272491,0.04939706,0.0002370432,0.00001033239,0.00001129932,0.00007721483,1.949146e-7,0.9473583,0.000322311,0.00008633281,0.002402726],"study_design_scores_gemma":[0.00137931,0.00005723122,0.01797513,0.001944975,0.00002813957,0.00001522222,0.00007844718,0.000151038,0.9573744,0.02047954,0.0002642958,0.0002522609],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750031,0.001386257,0.00951313,0.01178279,0.00004774966,0.000780648,0.00004859177,0.0000601354,0.001377645],"genre_scores_gemma":[0.922609,0.0006579483,0.0756178,0.00008403147,0.0001492486,0.0002874353,0.00000653679,0.00002346582,0.0005644924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06610467,"threshold_uncertainty_score":0.6483686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160933048271207,"score_gpt":0.3177112667441235,"score_spread":0.3016179619170029,"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."}}