{"id":"W3044308231","doi":"10.1142/s0219720020400077","title":"BENIN: Biologically enhanced network inference","year":2020,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Inference; Computer science; Data mining; Resampling; Gene regulatory network; Machine learning; Artificial intelligence; Biological data; Feature selection; Biological network; Computational biology; Bioinformatics; Biology; Gene; Gene expression","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":[0.003682889,0.001209554,0.001303354,0.002088385,0.001007052,0.001683713,0.003091994,0.00207006,0.009400335],"category_scores_gemma":[0.01075075,0.001204103,0.001618338,0.001420363,0.0008665204,0.002175229,0.00279575,0.003593203,0.002387558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296754,"about_ca_system_score_gemma":0.001712054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007249135,"about_ca_topic_score_gemma":0.01385792,"domain_scores_codex":[0.9986081,0.0007261117,0.000028213,0.0002503237,0.0003173926,0.00006998712],"domain_scores_gemma":[0.9959289,0.002856636,0.0002028041,0.0005833398,0.0002878322,0.0001405098],"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":[0.0003868975,0.0001821769,0.00338522,0.0004329805,0.0004083062,0.0002648604,0.0001843432,0.6802425,0.003358666,0.1095378,0.04025158,0.1613646],"study_design_scores_gemma":[0.00003560933,0.00001046217,0.0002002147,0.00001861634,0.00001555097,0.0000381512,0.00001103724,0.9403568,0.0006827836,0.05179006,0.006830829,0.00001001101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003708521,0.0003555965,0.9867906,0.0005558881,0.00009701639,0.00006920211,0.001195014,0.004825199,0.002402999],"genre_scores_gemma":[0.1201218,0.000429792,0.8636903,0.0006752072,0.0001319478,0.0004485342,0.004976742,0.001590601,0.007935109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009400335,"threshold_uncertainty_score":0.03144729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187800215957247,"score_gpt":0.2738799799279997,"score_spread":0.2550999583322749,"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."}}