{"id":"W2010854125","doi":"10.1186/s12859-014-0383-1","title":"Efficient prediction of human protein-protein interactions at a global scale","year":2014,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of Toronto; Carleton University","funders":"National Institute on Minority Health and Health Disparities; National Institute on Alcohol Abuse and Alcoholism; National Energy Research Scientific Computing Center; Sahlgrenska Akademin; Natural Sciences and Engineering Research Council of Canada; Office of Science; Saskatchewan Health Research Foundation; Linköpings Universitet; National Science Foundation; Canadian Institutes of Health Research; U.S. Department of Energy; National Institutes of Health; Vetenskapsrådet","keywords":"Computer science; Computational biology; Precision and recall; DNA microarray; Scale (ratio); Protein Interaction Networks; Range (aeronautics); Protein–protein interaction; Recall; Systems biology; Data mining; Machine learning; Artificial intelligence; Bioinformatics; Biology; Genetics; Gene; Engineering","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":[0.000644616,0.0008674192,0.0006543747,0.001385182,0.000299326,0.000615796,0.000430265,0.0005498464,0.001740801],"category_scores_gemma":[0.002956511,0.0003231766,0.0005542747,0.0008706097,0.0002312852,0.000874706,0.0008099238,0.0005912326,0.001100592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002887867,"about_ca_system_score_gemma":0.0004511859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179695,"about_ca_topic_score_gemma":0.001892398,"domain_scores_codex":[0.9996476,0.00008601529,0.00001510353,0.0001523775,0.00007543511,0.0000234772],"domain_scores_gemma":[0.9988505,0.0007122742,0.000127081,0.0001259018,0.0001318324,0.00005245641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001462642,0.0003136453,0.1359746,0.001314739,0.0008217459,0.001154289,0.0002890483,0.3648461,0.09396353,0.003696587,0.02609508,0.370068],"study_design_scores_gemma":[0.00005050515,0.0001523602,0.02983016,0.00003183895,0.0001135427,0.0006436294,0.00006872434,0.9398596,0.01648783,0.008227024,0.004509845,0.00002498551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5024312,0.002502135,0.4638236,0.0005018021,0.00004158029,0.000157704,0.01398899,0.0124055,0.00414749],"genre_scores_gemma":[0.8139611,0.0006604793,0.1665018,0.0001016937,0.00003960167,0.0001115663,0.01703776,0.0002977993,0.001288128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001740801,"threshold_uncertainty_score":0.005823612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00988622166715788,"score_gpt":0.2357234203278187,"score_spread":0.2258371986606608,"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."}}