{"id":"W3110553705","doi":"10.1093/nar/gkaa1067","title":"MarkerDB: an online database of molecular biomarkers","year":2020,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Institute of Genetics; Genome Alberta; Canada Foundation for Innovation; Genome Canada","keywords":"Biomarker; Biology; Biomarker discovery; Genetic marker; Computational biology; Molecular biomarkers; Proteomics; Bioinformatics; Database; Genetics; Gene; Oncology; Medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006233525,0.0001260484,0.0001516183,0.00006280429,0.00007308277,0.00002693135,0.0005422374,0.0001580276,0.0001253959],"category_scores_gemma":[0.0001510487,0.0001202246,0.00007401221,0.00028419,0.0002411866,0.000008362393,0.0004745664,0.0002523003,0.00002570582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001021343,"about_ca_system_score_gemma":0.0001190899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002519373,"about_ca_topic_score_gemma":0.00001137923,"domain_scores_codex":[0.9985036,0.000149373,0.0002775569,0.0003211185,0.0003657403,0.0003826052],"domain_scores_gemma":[0.9988629,0.0000157711,0.00005553875,0.000575903,0.0001961345,0.0002937695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003236148,0.0001063617,0.0004901659,0.00008268611,0.00008769993,0.00001142631,0.0001100824,0.00003146581,0.9656791,0.0002405911,0.01670624,0.01613056],"study_design_scores_gemma":[0.004548124,0.005776928,0.006324988,0.000143749,0.00006816869,0.00004241544,0.003454306,0.06011211,0.6652216,0.0008318058,0.2521474,0.001328456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898964,0.0007391805,0.004523071,0.00104952,0.00004846243,0.0002885733,0.0002242086,0.00001457931,0.003216041],"genre_scores_gemma":[0.991329,0.0002400448,0.006890926,0.0005627025,0.0001993803,0.000006429151,0.0006630415,0.00003246716,0.00007608172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3004575,"threshold_uncertainty_score":0.4902618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363109706227989,"score_gpt":0.3309349032336467,"score_spread":0.2773038061713669,"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."}}