{"id":"W2912743845","doi":"10.1104/pp.18.01216","title":"Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer","year":2019,"lang":"en","type":"article","venue":"PLANT PHYSIOLOGY","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Research in Immunology and Cancer; Université de Montréal; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Proteome; Computational biology; Protein–protein interaction; Chemistry; Biology; Computer science; Bioinformatics; Cell biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006604329,0.0001941658,0.0002438008,0.00007776541,0.00004110666,0.00001163284,0.0001906262,0.0001491011,0.0002469507],"category_scores_gemma":[0.00003476106,0.0001741881,0.0001207309,0.00008560041,0.00005014699,0.00001067474,0.00008153182,0.0002165427,0.00003823057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917892,"about_ca_system_score_gemma":0.0001205811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003250552,"about_ca_topic_score_gemma":0.00001967187,"domain_scores_codex":[0.9989347,0.00006073243,0.0003741783,0.0002977639,0.00009502678,0.0002375879],"domain_scores_gemma":[0.9991269,0.00001589209,0.0002586164,0.0004361922,0.00008859784,0.00007384273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002583956,0.00004846196,0.0001285173,0.00006848843,0.00008560311,4.697265e-7,0.00002007255,0.001260784,0.9962985,0.0002131416,0.0009562969,0.0006612539],"study_design_scores_gemma":[0.0008727434,0.0005035933,0.001554388,0.0001185798,0.00002639782,0.00001270991,0.00002277809,0.002681729,0.956143,0.001489649,0.036337,0.0002374301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990419,0.0001231083,0.007121058,0.0001185069,0.0004234108,0.0009469807,0.0002709801,0.0000180432,0.0005589165],"genre_scores_gemma":[0.9959332,0.000006256835,0.001781926,0.0002223715,0.0001763912,0.00005697345,0.001136226,0.00002065921,0.0006660219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04015552,"threshold_uncertainty_score":0.7103184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006237857357317348,"score_gpt":0.2119057837550021,"score_spread":0.2056679263976847,"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."}}