{"id":"W2223119478","doi":"10.1093/nar/gkv1115","title":"Integrated interactions database: tissue-specific view of the human and model organism interactomes","year":2015,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":268,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; Queen's University; University Health Network","funders":"","keywords":"Biology; Model organism; Organism; Computational biology; Protein–protein interaction; Genome; Database; Gene; Set (abstract data type); Human proteins; Genetics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005879218,0.0001041487,0.0001247003,0.00006255145,0.0001448576,0.00005332889,0.0003937032,0.00009055898,0.00005619305],"category_scores_gemma":[0.00007269766,0.00007271406,0.00003269416,0.0001915288,0.000281578,0.00001187984,0.000641655,0.0003667403,0.00001811737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002968766,"about_ca_system_score_gemma":0.000108603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002997079,"about_ca_topic_score_gemma":0.00004358923,"domain_scores_codex":[0.9990034,0.00009953106,0.0002346877,0.0001939909,0.0002378977,0.0002304673],"domain_scores_gemma":[0.9989775,0.00001891735,0.00005672712,0.0005338578,0.0003011889,0.0001118389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004860924,0.00007920317,0.0003891595,0.0000405605,0.00004713355,0.000001046658,0.0007951671,0.00005309583,0.908787,0.002306798,0.06763363,0.01981868],"study_design_scores_gemma":[0.001121192,0.0004174402,0.0006901071,0.0001790096,0.00001738598,0.00005371972,0.002459293,0.0131027,0.1907874,0.003181887,0.7876348,0.0003551155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862669,0.001323161,0.003610663,0.0005038081,0.0001668768,0.0003266992,0.00007235282,0.000009925473,0.00771963],"genre_scores_gemma":[0.9959748,0.0003046752,0.001378899,0.00006656954,0.00008856271,0.000009440014,0.0000899081,0.00001933285,0.002067858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7200012,"threshold_uncertainty_score":0.2965193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07572873190828375,"score_gpt":0.3607252206903182,"score_spread":0.2849964887820344,"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."}}