{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002938103,0.002135387,0.003742087,0.01021302,0.0008710278,0.00566793,0.003977705,0.002532003,0.05767427],"category_scores_gemma":[0.01196603,0.001246076,0.001327027,0.01117934,0.0003899368,0.003234143,0.003141854,0.001859074,0.05621574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495587,"about_ca_system_score_gemma":0.003412348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00448067,"about_ca_topic_score_gemma":0.003538888,"domain_scores_codex":[0.9980767,0.0003750052,0.0005071257,0.0003831391,0.0005098179,0.0001482132],"domain_scores_gemma":[0.9937364,0.002379002,0.001123276,0.0009623335,0.001106715,0.0006922912],"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":[0.001449778,0.0001840946,0.004032349,0.007225957,0.0004154194,0.0006221378,0.0001289188,0.00249874,0.004896028,0.01023618,0.8767905,0.09151999],"study_design_scores_gemma":[0.0003737065,0.0001128199,0.004142073,0.0008824125,0.0003147194,0.000630719,0.00009309709,0.002759861,0.004269364,0.01247174,0.9738348,0.0001147722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002206803,0.008562552,0.01579892,0.001217355,0.0002505432,0.000271933,0.9392307,0.02127352,0.01118762],"genre_scores_gemma":[0.00658595,0.004776888,0.0171616,0.0007414827,0.0001376347,0.0004156749,0.9667103,0.0009423493,0.002528193],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05767427,"threshold_uncertainty_score":0.1929396,"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."}}