{"id":"W3003975929","doi":"10.1101/2020.01.31.929570","title":"DELPHI: accurate deep ensemble model for protein interaction sites prediction","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Suite; Delphi; Deep learning; Source code; Code (set theory); Artificial intelligence; Machine learning; Ensemble learning; Data mining; Pattern recognition (psychology); Set (abstract data type)","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":[0.001381559,0.001242205,0.001396821,0.0009104212,0.000447828,0.0007917956,0.002319511,0.001439353,0.003291751],"category_scores_gemma":[0.00306888,0.0007553354,0.0009068435,0.0008165431,0.000406472,0.001431844,0.001424454,0.002415073,0.00107864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017505,"about_ca_system_score_gemma":0.00121669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009911911,"about_ca_topic_score_gemma":0.01221694,"domain_scores_codex":[0.9995444,0.0001400812,0.00001422461,0.0001327041,0.0001042492,0.00006433841],"domain_scores_gemma":[0.9991035,0.0004755184,0.00005625533,0.0001273551,0.0001550937,0.0000823029],"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":[0.000271084,0.0001338304,0.003093034,0.0001154443,0.0001838939,0.00009262722,0.00004011021,0.8843781,0.002255538,0.004200338,0.02615608,0.07907986],"study_design_scores_gemma":[0.000007225861,0.000006213063,0.00008796816,0.000002534748,0.00000328429,0.000005858587,0.000001806601,0.9977649,0.0002622282,0.001568143,0.0002875006,0.000002308579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1178923,0.002646982,0.8532697,0.001384343,0.0002069744,0.0001287178,0.005110926,0.01489762,0.004462448],"genre_scores_gemma":[0.6950489,0.0008900545,0.2731413,0.00110411,0.0002231369,0.0004935002,0.01976576,0.0009294981,0.008403781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009911911,"threshold_uncertainty_score":0.01970845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01938795160135867,"score_gpt":0.2435360676539371,"score_spread":0.2241481160525784,"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."}}