{"id":"W2153167299","doi":"10.1093/bioinformatics/btr035","title":"OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms","year":2011,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Identification (biology); Plug-in; Organism; Computer science; Protein–protein interaction; Computational biology; Model organism; Protein Interaction Networks; Biology; Genetics; Programming language; Gene; Ecology","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":[0.0001513327,0.0002149395,0.0003227243,0.00009193763,0.00007901763,0.00002856533,0.0001519705,0.0002020629,0.00003778905],"category_scores_gemma":[0.00003832496,0.0001588857,0.0001406017,0.0002154767,0.0001191157,0.0000140925,0.0002098456,0.0001019683,0.000004408753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001042433,"about_ca_system_score_gemma":0.00001939784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001488763,"about_ca_topic_score_gemma":0.00005688304,"domain_scores_codex":[0.9989349,0.00001967756,0.0005655384,0.0001370478,0.00008469232,0.0002581832],"domain_scores_gemma":[0.9991564,0.00002999287,0.0003249684,0.0003054356,0.0000882628,0.00009498523],"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":[0.001752915,0.001680645,0.1129003,0.0009571744,0.01430125,0.00001670947,0.09997808,0.003972665,0.08133408,0.001588293,0.001212673,0.6803052],"study_design_scores_gemma":[0.002510414,0.00315661,0.05092549,0.00008348871,0.00142871,0.0001228947,0.02669618,0.7591202,0.1468084,0.0003243674,0.007049577,0.00177367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457866,0.0001230763,0.05243071,0.000007419532,0.0001409244,0.0001452742,0.00008611646,0.00001536738,0.001264545],"genre_scores_gemma":[0.9963866,0.0002205552,0.002829672,0.0002009424,0.00003700016,0.000004267433,0.0002544975,0.00001370962,0.00005276823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7551476,"threshold_uncertainty_score":0.6479172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239312957601071,"score_gpt":0.2534325284217612,"score_spread":0.2410393988457505,"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."}}