{"id":"W2080257751","doi":"10.1016/j.jprot.2014.03.018","title":"In-depth proteomic delineation of the colorectal cancer exoproteome: Mechanistic insight and identification of potential biomarkers","year":2014,"lang":"en","type":"article","venue":"Journal of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Colorectal cancer; Cancer; Identification (biology); Computational biology; Proteomics; Medicine; Biology; Bioinformatics; Internal medicine; Genetics","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":[0.0005181906,0.0001334877,0.0002878037,0.000121115,0.00006185259,0.0000161048,0.0003016023,0.0001385415,0.00001975598],"category_scores_gemma":[0.0001957985,0.0001041563,0.0001029586,0.0002188872,0.0001324353,0.0001690479,0.00007461327,0.0002713597,2.609556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001104851,"about_ca_system_score_gemma":0.0001252505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005130096,"about_ca_topic_score_gemma":0.00001543677,"domain_scores_codex":[0.9984481,0.00004428693,0.0009771484,0.0001557082,0.0002459804,0.0001288241],"domain_scores_gemma":[0.9976679,0.00003167729,0.001711945,0.0002171015,0.0003270801,0.00004427229],"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":[0.0001666703,0.00006616833,0.001501497,0.0001460721,0.00002782836,4.121763e-7,0.00009702425,0.0004520197,0.9947478,0.001118865,0.00001031524,0.001665361],"study_design_scores_gemma":[0.0006231394,0.00005626967,0.00210848,0.0001641384,0.0000486879,0.00003160862,0.00003451726,0.006270193,0.9803695,0.01014406,0.0000486585,0.0001007638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7981105,0.00005961698,0.2008981,0.0002891262,0.0000606665,0.0005120467,0.0000198265,0.000006042442,0.00004416464],"genre_scores_gemma":[0.9526053,0.0001590663,0.04694292,0.00001317543,0.00009874571,0.0001201307,0.00000206437,0.00002008252,0.00003848677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1544949,"threshold_uncertainty_score":0.4247371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007529591896645045,"score_gpt":0.2611105938562519,"score_spread":0.2535810019596069,"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."}}