{"id":"W2911960890","doi":"10.1109/bibm.2018.8621551","title":"A Deep Learning Framework for Identifying Essential Proteins Based on Protein-Protein Interaction Network and Gene Expression Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; AdaBoost; Support vector machine; Random forest; Identification (biology); Decision tree; Network topology; Deep learning; Feature selection; Biology","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":[0.0005275958,0.000941076,0.0008960236,0.001518033,0.000355798,0.0006833024,0.001358444,0.0009281146,0.001109438],"category_scores_gemma":[0.0007071422,0.0004175024,0.0009113703,0.001266988,0.0004494534,0.001034472,0.0008057031,0.00130677,0.0004208018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334923,"about_ca_system_score_gemma":0.001435148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0143449,"about_ca_topic_score_gemma":0.02032816,"domain_scores_codex":[0.9998065,0.00002774398,0.00001034361,0.00006797447,0.00005029893,0.0000371999],"domain_scores_gemma":[0.9998167,0.00005867688,0.00003090572,0.00001798363,0.00005116703,0.00002465137],"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":[0.0001932548,0.0002900598,0.004865073,0.000187592,0.0001834884,0.0002530628,0.00006172356,0.7454392,0.01125048,0.01662141,0.006502257,0.2141524],"study_design_scores_gemma":[0.00000425497,0.00001308166,0.0002625402,0.000003743064,0.000007773646,0.0000177497,0.000003361747,0.9943649,0.0006179659,0.004215242,0.000485189,0.000004296378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02641096,0.001037066,0.9674348,0.0004105702,0.00003958193,0.00006445561,0.001391322,0.001991546,0.001219672],"genre_scores_gemma":[0.564644,0.001815976,0.4195466,0.0004680984,0.0001062805,0.000443327,0.006579139,0.0001505611,0.00624609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0143449,"threshold_uncertainty_score":0.02852279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06426970783534207,"score_gpt":0.3717219790427979,"score_spread":0.3074522712074558,"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."}}