{"id":"W3004627960","doi":"10.1101/163907","title":"A user’s guide to the online resources for data exploration, visualization, and discovery for the Pan-Cancer Analysis of Whole Genomes project (PCAWG)","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Ontario Institute for Cancer Research","funders":"","keywords":"Genomics; Visualization; Resource (disambiguation); Genome; Data visualization; Genome browser; Chromothripsis; Data exploration","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006360529,0.003174084,0.00272663,0.006684082,0.001229799,0.00374903,0.005143394,0.002243287,0.4328537],"category_scores_gemma":[0.02681017,0.002821707,0.001787266,0.01110652,0.001019558,0.004523669,0.005513589,0.004480999,0.3704106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403252,"about_ca_system_score_gemma":0.004398315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00585587,"about_ca_topic_score_gemma":0.01182016,"domain_scores_codex":[0.996895,0.0005356339,0.0005504442,0.0004662908,0.001272081,0.0002806032],"domain_scores_gemma":[0.9803948,0.009434012,0.000867239,0.003641915,0.003790013,0.001872069],"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":[0.00008928376,0.00004195191,0.0002378547,0.0004814852,0.00002133961,0.0001114678,0.0000902081,0.0001636071,0.000786724,0.001154308,0.9713018,0.02552002],"study_design_scores_gemma":[0.0001766743,0.00002019438,0.001518473,0.0004083521,0.0000195493,0.0002884273,0.00007457809,0.000986149,0.002042573,0.006364069,0.9880144,0.00008661884],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.0008139494,0.001098117,0.0977205,0.002127563,0.0007244797,0.001436631,0.577172,0.2839316,0.03497513],"genre_scores_gemma":[0.005501735,0.001903674,0.2972317,0.004718763,0.0005448562,0.005870236,0.488776,0.1513026,0.04415033],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4328537,"threshold_uncertainty_score":0.8089653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575052070282032,"score_gpt":0.3123775312022365,"score_spread":0.2766270104994162,"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."}}