{"id":"W4200607868","doi":"10.1038/s41598-021-02282-3","title":"Systems biology and machine learning approaches identify drug targets in diabetic nephropathy","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Institute for Medical Research Development; Isfahan University of Medical Sciences","keywords":"Machine learning; Computational biology; Renal cortex; Computer science; Diabetic nephropathy; Artificial intelligence; Bioinformatics; Medulla; Drug discovery; Proteome; Biology; Medicine; Kidney; Internal medicine; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00115895,0.0009137034,0.0009309395,0.002214675,0.0003375151,0.001056542,0.0005059147,0.0006659654,0.001157269],"category_scores_gemma":[0.001481896,0.0003064338,0.0008333912,0.001018867,0.0003433715,0.0006038524,0.0005342397,0.0006573962,0.0003459586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008654094,"about_ca_system_score_gemma":0.0008075325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001310454,"about_ca_topic_score_gemma":0.001328248,"domain_scores_codex":[0.9996431,0.0001144511,0.00002951559,0.00009881946,0.00008137665,0.0000327198],"domain_scores_gemma":[0.9995646,0.0002298692,0.00009003294,0.00003018744,0.00006504686,0.0000201644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005033447,0.0004641362,0.03002013,0.001801656,0.0008005625,0.0005018935,0.0001294911,0.3388979,0.08022255,0.01540892,0.003350077,0.5278994],"study_design_scores_gemma":[0.00002529687,0.000272334,0.006295999,0.00005944706,0.0001459194,0.0001374152,0.00003759738,0.9587453,0.01582282,0.01468629,0.003743834,0.00002770809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2236913,0.02699019,0.7322061,0.002372715,0.0002692725,0.000448362,0.001686514,0.00339911,0.008936388],"genre_scores_gemma":[0.8096972,0.006550726,0.1799967,0.000578268,0.0001329567,0.0002876473,0.001164568,0.00005544116,0.001536476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002214675,"threshold_uncertainty_score":0.006279051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1456437355701742,"score_gpt":0.4171369927614775,"score_spread":0.2714932571913033,"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."}}