{"id":"W4224008607","doi":"10.1002/path.5905","title":"Integrating computational pathology and proteomics to address tumor heterogeneity","year":2022,"lang":"en","type":"review","venue":"The Journal of Pathology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Brain Tumour Foundation of Canada; Terry Fox Research Institute; Ontario Institute for Cancer Research","keywords":"Proteomics; Proteogenomics; Computational biology; Precision medicine; Data science; Computer science; Biology; Bioinformatics; Medicine; Pathology; Genomics; Genome","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":[0.0008257977,0.0002591666,0.0009522153,0.0001146624,0.0002164542,0.00001621149,0.0006181952,0.00014674,0.000127858],"category_scores_gemma":[0.0001822139,0.0001754321,0.0002022532,0.0001320159,0.0001350231,0.00003267754,0.0004095357,0.001239936,0.00000343471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001224135,"about_ca_system_score_gemma":0.0001631784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001562408,"about_ca_topic_score_gemma":0.000002102777,"domain_scores_codex":[0.9983414,0.0002635926,0.0008343526,0.0002106272,0.0001419796,0.0002080011],"domain_scores_gemma":[0.9977961,0.0004717199,0.001258238,0.000305358,0.00008460222,0.00008402097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001014739,0.0001901374,0.00001831558,0.004065248,0.0001973265,0.001413082,0.0007018978,0.0004532572,0.003183136,0.003365178,0.0004230776,0.9858879],"study_design_scores_gemma":[0.0002275885,0.0002414971,0.000001616652,0.001156123,0.0004710881,0.05934863,0.0001394049,0.0000353063,0.0004637504,0.006880001,0.9306599,0.0003751282],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005850653,0.9485378,0.04409357,0.0001614411,0.00009945918,0.0006954109,0.0002310895,0.00003667126,0.0002939234],"genre_scores_gemma":[0.0001464973,0.9078229,0.09127795,0.0001866593,0.0002050942,0.0002414554,0.00003282551,0.00005313001,0.00003351104],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9855127,"threshold_uncertainty_score":0.7153915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05244790506074915,"score_gpt":0.3655013408201079,"score_spread":0.3130534357593587,"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."}}