{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002119626,0.001319812,0.001750563,0.002406091,0.0002102334,0.001998012,0.001499499,0.001652605,0.001765655],"category_scores_gemma":[0.002336434,0.0004850552,0.000805367,0.001680811,0.001131602,0.001999444,0.001804869,0.002845659,0.00146786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009266501,"about_ca_system_score_gemma":0.001490782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000782587,"about_ca_topic_score_gemma":0.0009169587,"domain_scores_codex":[0.9995754,0.000127323,0.0000298051,0.00008469833,0.0001510055,0.00003173529],"domain_scores_gemma":[0.9985713,0.0009651435,0.00007779298,0.00006477952,0.0002435434,0.00007731991],"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":[0.00005567602,0.00006041949,0.000419074,0.007965884,0.0003107963,0.0002051697,0.00006788691,0.003990449,0.002673929,0.02612751,0.02442413,0.933699],"study_design_scores_gemma":[0.00003965748,0.0001521946,0.001230888,0.004262493,0.0002445585,0.001973312,0.00008617941,0.005402609,0.003439302,0.05625851,0.9268232,0.00008708216],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003808072,0.9836268,0.01127123,0.001839988,0.0005235465,0.00001675563,0.00003659162,0.0001199873,0.002184356],"genre_scores_gemma":[0.005021731,0.9827322,0.009149432,0.0009710629,0.0008559537,0.00003216425,0.000118427,0.00003831186,0.001080671],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002406091,"threshold_uncertainty_score":0.01120979,"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."}}