{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001814663,0.001298079,0.001018773,0.005167812,0.000340897,0.0007295551,0.0005547735,0.0006145044,0.0009180719],"category_scores_gemma":[0.004849941,0.0002467372,0.0005787042,0.00149847,0.0002733211,0.0007981384,0.0007664597,0.0005731318,0.0003831654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003911216,"about_ca_system_score_gemma":0.0008288508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004334738,"about_ca_topic_score_gemma":0.006350752,"domain_scores_codex":[0.9992229,0.0002419689,0.00005262486,0.0002232839,0.0001792836,0.00007994605],"domain_scores_gemma":[0.997906,0.001230119,0.0003516112,0.0001751967,0.0001959894,0.0001409966],"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":[0.001890302,0.0009097643,0.4599652,0.0003648636,0.001035534,0.0005983241,0.000152976,0.1380711,0.02686551,0.002175593,0.005039257,0.3629316],"study_design_scores_gemma":[0.00005135346,0.0003590337,0.05194123,0.00002360958,0.0001396296,0.0004525019,0.00004495776,0.933992,0.007230295,0.004208661,0.001503068,0.00005370721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6602265,0.001627615,0.3208496,0.0005440815,0.00008641397,0.0003970281,0.008941445,0.005735219,0.001592076],"genre_scores_gemma":[0.8873942,0.000278954,0.1075662,0.00005793093,0.00006312376,0.0001567625,0.003821123,0.00004902464,0.0006126085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005167812,"threshold_uncertainty_score":0.009596944,"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."}}