{"id":"W4404328518","doi":"10.1093/nar/gkae1056","title":"MarkerDB 2.0: a comprehensive molecular biomarker database for 2025","year":2024,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Canada Foundation for Innovation; Genome Canada","keywords":"Biomarker; Usability; Resource (disambiguation); Consistency (knowledge bases); Molecular biomarkers; Computer science; Data science; Biomarker discovery; Database; Biology; Medicine; Proteomics; Human–computer interaction","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":[0.006116337,0.001360486,0.00224538,0.007032224,0.0009037697,0.006267651,0.003912662,0.001968004,0.03998829],"category_scores_gemma":[0.0182565,0.001391377,0.0012594,0.006395622,0.0004033589,0.005118321,0.004578997,0.002136261,0.05463709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00157074,"about_ca_system_score_gemma":0.005331864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005004986,"about_ca_topic_score_gemma":0.004192166,"domain_scores_codex":[0.9977043,0.0004756105,0.0005092837,0.0003378047,0.000768209,0.0002047406],"domain_scores_gemma":[0.9932021,0.001569284,0.001062592,0.001090565,0.001699288,0.001376149],"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":[0.0007940827,0.00006819107,0.002742029,0.001471433,0.0001489061,0.0001783083,0.0001024984,0.0006286398,0.002295341,0.005340491,0.9108508,0.07537927],"study_design_scores_gemma":[0.0002010418,0.00007878019,0.002930774,0.0004990996,0.0001277998,0.0003623833,0.00007317709,0.001512926,0.002740178,0.006617816,0.984762,0.00009405358],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005346011,0.0144095,0.06525064,0.008883907,0.001497507,0.00073061,0.7856361,0.09512533,0.02312044],"genre_scores_gemma":[0.01077739,0.004814788,0.05544912,0.003612567,0.0004176133,0.0007457585,0.9144422,0.004088043,0.005652518],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03998829,"threshold_uncertainty_score":0.1337741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07912278611766696,"score_gpt":0.4169082417017413,"score_spread":0.3377854555840744,"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."}}