{"id":"W3162343445","doi":"10.1093/bioinformatics/btab363","title":"PaIntDB: network-based omics integration and visualization using protein–protein interactions in <i>Pseudomonas aeruginosa</i>","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Interactome; Visualization; Computer science; Source code; Computational biology; Pseudomonas aeruginosa; Web server; Data mining; Bioinformatics; Biology; World Wide Web; The Internet; Genetics; Gene; Programming language","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":[0.001075667,0.001744077,0.001216513,0.002665714,0.00106235,0.002731668,0.002502477,0.0009042249,0.04985386],"category_scores_gemma":[0.00271406,0.001039685,0.001562837,0.002309192,0.0003856672,0.002161182,0.003237331,0.001942418,0.0132851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009213068,"about_ca_system_score_gemma":0.00163171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006942391,"about_ca_topic_score_gemma":0.008479224,"domain_scores_codex":[0.9993299,0.00007850699,0.00004524998,0.0001674591,0.0002897976,0.00008904801],"domain_scores_gemma":[0.9993321,0.0002247999,0.00008240208,0.0001110079,0.0001244868,0.0001250741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001792837,0.0002084166,0.00670001,0.004979443,0.0007404981,0.001046536,0.0009331661,0.008356611,0.103972,0.01019082,0.7779083,0.0831714],"study_design_scores_gemma":[0.001387827,0.000359969,0.03757316,0.001244495,0.0003726067,0.001790819,0.0006148035,0.1860826,0.08317018,0.03109124,0.6557198,0.0005924954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02704093,0.001444518,0.1643673,0.001532585,0.0005459799,0.0005540658,0.4644487,0.3244519,0.01561401],"genre_scores_gemma":[0.1008593,0.001975889,0.259414,0.0008137534,0.0001388462,0.001913611,0.5851241,0.04066141,0.009098959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04985386,"threshold_uncertainty_score":0.1667778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471264612648251,"score_gpt":0.2561034443709633,"score_spread":0.2413907982444808,"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."}}