{"id":"W3010830252","doi":"10.1186/s12859-020-3346-8","title":"Ensemble disease gene prediction by clinical sample-based networks","year":2020,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"DNA microarray; Computational biology; Ensemble learning; Computer science; Disease; Artificial intelligence; Gene; Bioinformatics; Genetics; Biology; Medicine; Gene expression; Pathology","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":[0.0003215992,0.0002528881,0.0002411828,0.00002227583,0.0001266509,0.00007965435,0.0003049395,0.0002925244,0.00002507416],"category_scores_gemma":[0.0002135161,0.0002369266,0.0002271154,0.0001285916,0.0001095952,0.0000147172,0.000145312,0.0002156942,0.00004908326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001377724,"about_ca_system_score_gemma":0.0001904121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002410293,"about_ca_topic_score_gemma":0.000003402021,"domain_scores_codex":[0.9982491,0.00004975183,0.0008788069,0.0002372722,0.000197038,0.0003879982],"domain_scores_gemma":[0.9985662,0.00005944772,0.0002711123,0.0004550189,0.00007756624,0.0005706726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00181627,0.0003951595,0.04618213,0.0006621754,0.0003044782,0.000003779221,0.0002523769,0.1905787,0.001779105,0.0002910927,0.7151392,0.04259554],"study_design_scores_gemma":[0.001108389,0.0003340317,0.000754686,0.0000122688,0.00005006733,0.000001680729,0.00004130214,0.9193994,0.0005643089,0.00003602063,0.07741642,0.0002814299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009656793,0.0004579446,0.9878763,0.0002843776,0.0003404833,0.0003960585,0.0004263945,0.00006306763,0.0004985833],"genre_scores_gemma":[0.6389914,0.000880369,0.3202265,0.02457083,0.003117351,0.00008498204,0.01177582,0.0001216924,0.0002310526],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7288207,"threshold_uncertainty_score":0.9661586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02487448227903037,"score_gpt":0.2560171676417269,"score_spread":0.2311426853626965,"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."}}