{"id":"W2883188243","doi":"10.1101/369850","title":"Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Genome British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computational biology; Genome; Biology; Gene; Transcriptome; Genetics; Gene expression","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":[0.0007796235,0.0006823576,0.0006451083,0.005943675,0.0006532653,0.001625601,0.0006187582,0.0004845666,0.002139559],"category_scores_gemma":[0.003457581,0.0003137181,0.0006393763,0.002941895,0.000774331,0.0007358126,0.001352533,0.0006544053,0.0003445417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006796488,"about_ca_system_score_gemma":0.0007906981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001724693,"about_ca_topic_score_gemma":0.003123089,"domain_scores_codex":[0.9992513,0.0001582506,0.00004716267,0.0003062503,0.0001704964,0.00006659089],"domain_scores_gemma":[0.9978253,0.000904451,0.0004135945,0.0003694396,0.0002731991,0.0002140437],"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":[0.001579454,0.0005940543,0.2070478,0.001231176,0.001199986,0.001108908,0.0004333827,0.1187773,0.3316444,0.02256,0.008093101,0.3057305],"study_design_scores_gemma":[0.00005682189,0.0001448356,0.05812749,0.00002821129,0.000210533,0.0007322252,0.0001270502,0.8716684,0.04653338,0.01840673,0.003906701,0.00005759189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5860786,0.000697874,0.3985609,0.0002856961,0.00003578569,0.0002303529,0.005381665,0.004271314,0.004457851],"genre_scores_gemma":[0.8909269,0.0001024351,0.1048551,0.00005879188,0.00003606304,0.0001381262,0.00320991,0.0001170728,0.0005555901],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005943675,"threshold_uncertainty_score":0.007157564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01702479451160802,"score_gpt":0.2460947958467815,"score_spread":0.2290700013351735,"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."}}