{"id":"W2740846483","doi":"10.1158/1538-7445.am2017-2607","title":"Abstract 2607: The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine","year":2017,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Genomics; Precision medicine; Personalized medicine; Big data; Medicine; Data science; Cancer; Bioinformatics; Computer science; Genome; Biology; Data mining; Genetics; Pathology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.004927656,0.002275732,0.001831537,0.006412787,0.001498911,0.005488728,0.00566574,0.002630766,0.06420245],"category_scores_gemma":[0.01518022,0.001752784,0.00212437,0.006412729,0.001019176,0.004306548,0.01044376,0.004556646,0.07755178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001427448,"about_ca_system_score_gemma":0.004644742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006987548,"about_ca_topic_score_gemma":0.007034075,"domain_scores_codex":[0.9971914,0.0005450913,0.0002997724,0.0005979331,0.001077744,0.0002880591],"domain_scores_gemma":[0.9925759,0.002108873,0.0005087022,0.001975265,0.001542182,0.001289119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004242524,0.00004582704,0.001044555,0.001137497,0.0001156101,0.0002520713,0.0001892484,0.001111375,0.004462061,0.005345392,0.9478205,0.03805153],"study_design_scores_gemma":[0.0007581319,0.00007364075,0.004546712,0.0008887587,0.0001859963,0.0007585108,0.0001531463,0.0120713,0.009328081,0.03218933,0.9387931,0.0002533629],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.003029992,0.002579693,0.1604862,0.003327236,0.001079563,0.0007622149,0.4363352,0.3733308,0.01906907],"genre_scores_gemma":[0.01211577,0.001451647,0.1232185,0.001726387,0.000353765,0.001429836,0.776509,0.07537568,0.007819452],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.9943343,"threshold_uncertainty_score":0.2147786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2413719756795527,"score_gpt":0.5013311722427092,"score_spread":0.2599591965631565,"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."}}