{"id":"W2953873015","doi":"10.1016/j.jtbi.2019.06.022","title":"Predicting essential proteins from protein-protein interactions using order statistics","year":2019,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Tianjin City; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Statistics; Computer science; Computational biology; Biology; Mathematics","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":[0.0008937884,0.0006004818,0.001032856,0.002977325,0.0005095406,0.0008704778,0.0005461583,0.0005637928,0.00093527],"category_scores_gemma":[0.00348944,0.000413631,0.001099037,0.001109729,0.0005208505,0.001133287,0.0006081382,0.0009513996,0.0003454618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007273479,"about_ca_system_score_gemma":0.00141418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002216636,"about_ca_topic_score_gemma":0.004425806,"domain_scores_codex":[0.9996296,0.00009742881,0.00003260463,0.00005001836,0.0001258484,0.00006442721],"domain_scores_gemma":[0.9965029,0.002392307,0.0004167475,0.0002177384,0.000246972,0.0002233837],"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":[0.002227659,0.0008861828,0.1307571,0.0006111324,0.0007192673,0.001327879,0.0001692834,0.6175658,0.05585744,0.04944968,0.006035861,0.1343928],"study_design_scores_gemma":[0.00003670834,0.0001063558,0.006901399,0.000008824936,0.00006882623,0.0001459186,0.00002256122,0.9549666,0.004218813,0.03308502,0.0004221211,0.00001694781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6642603,0.001086832,0.3297817,0.0002640117,0.0000600708,0.0000884601,0.001250073,0.001323717,0.001884744],"genre_scores_gemma":[0.9703007,0.0004101079,0.02726731,0.00004891365,0.00005077724,0.00003792627,0.001197447,0.0000876479,0.0005991926],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002977325,"threshold_uncertainty_score":0.005277276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01433765341634247,"score_gpt":0.3241848465935959,"score_spread":0.3098471931772535,"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."}}