{"id":"W2951296328","doi":"10.1093/gigascience/giz024","title":"Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks","year":2019,"lang":"en","type":"article","venue":"GigaScience","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Genome British Columbia; Simon Fraser University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; International Business Machines Corporation; National Science Foundation","keywords":"Computational biology; Biology; Gene; Genome; Identification (biology); Conserved sequence; Genetics; Base sequence","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":[0.0005708614,0.0007247394,0.0005457901,0.005079227,0.0006123792,0.001114927,0.0005923392,0.0003945626,0.002043353],"category_scores_gemma":[0.00274201,0.0002614534,0.0007004914,0.003144828,0.0006648375,0.00067221,0.001293789,0.000588461,0.0003838957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006316449,"about_ca_system_score_gemma":0.0007118566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001763025,"about_ca_topic_score_gemma":0.003722117,"domain_scores_codex":[0.9994699,0.00008473924,0.00003361564,0.0002062042,0.0001519977,0.00005346124],"domain_scores_gemma":[0.9984984,0.0006091511,0.000334714,0.0002394526,0.000158728,0.0001594166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00121719,0.0005376089,0.1724391,0.001312264,0.001068716,0.00140083,0.000493282,0.0990809,0.3790227,0.0187203,0.008426455,0.3162808],"study_design_scores_gemma":[0.00007489769,0.0002428769,0.1164624,0.00004237212,0.0003611897,0.001545329,0.0002095392,0.7697343,0.06980871,0.03335451,0.008068553,0.00009528355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6076539,0.0008857252,0.3743412,0.0002932575,0.00003515066,0.0002650513,0.006725249,0.004975785,0.004824741],"genre_scores_gemma":[0.8800462,0.0001721667,0.1138416,0.00007620828,0.00003127919,0.0001728956,0.004938713,0.0001834548,0.0005375795],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005079227,"threshold_uncertainty_score":0.006835759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443653803342532,"score_gpt":0.2642963422038783,"score_spread":0.2498598041704529,"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."}}