{"id":"W4410565760","doi":"10.53555/sfs.v8i3.3605","title":"Machine Learning-Driven Biomarker Discovery in Chronic Kidney Disease for Personalized Therapeutic Strategies","year":2021,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomarker; Biomarker discovery; Kidney disease; Personalized medicine; Disease; Medicine; Computational biology; Bioinformatics; Computer science; Internal medicine; Biology; Proteomics; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006047601,0.0001621711,0.0004294279,0.0002385273,0.0005654622,0.0001263788,0.0003919832,0.0001123704,0.0004767662],"category_scores_gemma":[0.005082926,0.0001255318,0.0001168714,0.0009210844,0.0005842037,0.001215567,0.00007954258,0.0007011305,0.000005391951],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006152929,"about_ca_system_score_gemma":0.01050542,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003281382,"about_ca_topic_score_gemma":0.04907954,"domain_scores_codex":[0.9950207,0.002465603,0.001086434,0.0002785843,0.0005708061,0.0005778274],"domain_scores_gemma":[0.9957618,0.002682978,0.0005567166,0.0001354501,0.0006434048,0.0002196499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004647657,0.00005669453,0.9934964,0.0002139806,0.00001271627,0.00004217778,0.002493719,0.0007646985,0.0003606962,0.0008681098,0.0004404701,0.000785571],"study_design_scores_gemma":[0.0007796782,0.0004397608,0.9460348,0.0009776835,0.00001409638,0.000006356098,0.02186335,0.00718572,0.00005934564,0.00647488,0.01590153,0.0002628167],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825438,0.005131047,0.0008091569,0.008780478,0.001638002,0.0004937652,0.0001246544,0.00001454629,0.0004645255],"genre_scores_gemma":[0.9971313,0.001079847,0.0002115173,0.0005431797,0.0001534614,0.00003372362,0.00001799856,0.00001559545,0.000813397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04746162,"threshold_uncertainty_score":0.9951041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4040859334795851,"score_gpt":0.4624606229309218,"score_spread":0.05837468945133673,"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."}}