{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00174053,0.001184049,0.0009275639,0.002122633,0.0003743664,0.0006657172,0.0008159143,0.0008540741,0.000777897],"category_scores_gemma":[0.00454362,0.0003246475,0.0009142343,0.001074436,0.0003530828,0.001024718,0.000665782,0.000913612,0.0002662079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007299734,"about_ca_system_score_gemma":0.000643616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005265638,"about_ca_topic_score_gemma":0.006508203,"domain_scores_codex":[0.9992313,0.0002159816,0.00004529344,0.0002747261,0.0001704039,0.00006226084],"domain_scores_gemma":[0.9971389,0.001691464,0.0003433188,0.0002334295,0.0004616187,0.0001314565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005286461,0.0002669101,0.09446438,0.0001197218,0.0005295155,0.0005805621,0.00009920404,0.6791438,0.00441915,0.001250053,0.003588224,0.2150098],"study_design_scores_gemma":[0.000009039968,0.00003499985,0.002907171,0.000009407414,0.00005723001,0.0001013368,0.00001208879,0.9937582,0.0009567031,0.001777058,0.0003700289,0.000006713911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3989302,0.00260203,0.5917646,0.0009165258,0.0001128066,0.0001926448,0.001650632,0.001474333,0.002356136],"genre_scores_gemma":[0.918578,0.0005752753,0.07704517,0.0001749967,0.0001161501,0.00009634089,0.002644824,0.00004312368,0.0007261229],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005265638,"threshold_uncertainty_score":0.01047003,"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."}}