{"id":"W4213415552","doi":"10.1093/bib/bbac090","title":"cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines","year":2022,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency; BC Children's Hospital; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Research Chairs; BC Children's Hospital","keywords":"Biomarker; Resource (disambiguation); Computer science; Web resource; Cancer; Computational biology; World Wide Web; Medicine; Biology; Internal medicine; Genetics; Computer network","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":[0.002133362,0.002780247,0.002434494,0.007511634,0.0007434775,0.002806709,0.003261868,0.001586517,0.03677364],"category_scores_gemma":[0.005850365,0.001118181,0.001935597,0.00560088,0.0003833009,0.001984185,0.004000118,0.001949668,0.02853288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194148,"about_ca_system_score_gemma":0.002099846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004274008,"about_ca_topic_score_gemma":0.007782881,"domain_scores_codex":[0.9987286,0.0001871113,0.0001995805,0.0002924589,0.0004664304,0.0001258397],"domain_scores_gemma":[0.996052,0.001703911,0.000540767,0.0007647478,0.0005071516,0.0004315838],"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":[0.002216012,0.0002828214,0.02599196,0.009310634,0.001292244,0.002101096,0.0006028968,0.005233837,0.02463305,0.006571774,0.8355604,0.08620322],"study_design_scores_gemma":[0.0008471841,0.0003254917,0.0358115,0.001155549,0.0006720223,0.002027983,0.0002925697,0.02471849,0.03030806,0.02047732,0.8828612,0.0005026991],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.006840532,0.00276123,0.02456725,0.0004312716,0.0002018561,0.0002378692,0.8118432,0.1484383,0.004678478],"genre_scores_gemma":[0.02786373,0.001768613,0.02969544,0.0008191081,0.00009569328,0.001026864,0.9209811,0.01496052,0.002788942],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03677364,"threshold_uncertainty_score":0.1230201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910551591513251,"score_gpt":0.2789300202867843,"score_spread":0.2598245043716517,"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."}}