{"id":"W2027667376","doi":"10.1371/journal.pcbi.1001114","title":"CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data","year":2011,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of British Columbia","funders":"Michael Smith Health Research BC; Canadian Institutes of Health Research; Government of Ontario; Ontario Institute for Cancer Research; Howard Hughes Medical Institute","keywords":"Computational biology; Biology; Gene; Gene signature; Bayes' theorem; Carcinogenesis; Context (archaeology); Cancer; Classifier (UML); Bioinformatics; Genetics; Gene expression; Computer science; Bayesian probability; Artificial intelligence","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":[0.0002723435,0.0001811292,0.0001829222,0.00006354359,0.0002448756,0.00004069077,0.0004459651,0.0002206479,0.00001157594],"category_scores_gemma":[0.0001636384,0.0001597778,0.00002361926,0.00007987573,0.0001107897,0.0001002989,0.0007086341,0.0001577524,0.000002396925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001766876,"about_ca_system_score_gemma":0.0001198542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009533711,"about_ca_topic_score_gemma":0.00002151931,"domain_scores_codex":[0.9987261,0.0000960257,0.0004988285,0.0003396223,0.0001096685,0.0002297591],"domain_scores_gemma":[0.998903,0.0000546315,0.0003447473,0.0004931383,0.0001261458,0.00007836097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001159726,0.00003051634,0.9722682,0.00009895485,0.0003272326,0.000001078191,0.0005085102,0.0123714,0.00645332,0.001344258,0.0002059033,0.006274674],"study_design_scores_gemma":[0.001458552,0.0002017286,0.2389117,0.0001174154,0.0001365112,0.00002530081,0.0001527919,0.7413406,0.002471452,0.01414154,0.0003721292,0.0006703222],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8061392,0.0002982603,0.1920384,0.0000613813,0.00007826525,0.0005264931,0.0008082661,0.00002161077,0.00002816928],"genre_scores_gemma":[0.9231574,0.000003987132,0.06144446,0.00009503704,0.0002432587,0.00002711644,0.01500514,0.00001307052,0.00001052196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7333565,"threshold_uncertainty_score":0.6515548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08386385041954844,"score_gpt":0.3021927733435701,"score_spread":0.2183289229240216,"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."}}