{"id":"W3165113039","doi":"10.1186/s12859-021-04042-6","title":"PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics","year":2021,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Government of Ontario; Compute Canada","keywords":"Proteomics; Computational biology; Interaction network; Cluster analysis; Biology; DNA microarray; Annotation; Gene ontology; Quantitative proteomics; Bioinformatics; Computer science; Gene expression; Gene; Genetics; Artificial intelligence","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":[0.002031614,0.001746053,0.001302262,0.002083865,0.0004838649,0.00106337,0.001716483,0.0009865594,0.002304648],"category_scores_gemma":[0.003098146,0.0006232451,0.00246229,0.001184214,0.0009101571,0.0006369562,0.001198787,0.001877902,0.0008667504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000843054,"about_ca_system_score_gemma":0.001112937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001459105,"about_ca_topic_score_gemma":0.002321115,"domain_scores_codex":[0.9990563,0.0003124047,0.00002794928,0.0002133378,0.0003343371,0.00005574549],"domain_scores_gemma":[0.9984362,0.0009834288,0.0001619543,0.0001764289,0.0001697461,0.00007228181],"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":[0.001271054,0.0008540547,0.01150296,0.002487967,0.00206232,0.001254722,0.0002877871,0.269176,0.3076761,0.03789064,0.02385083,0.3416855],"study_design_scores_gemma":[0.0000585642,0.0001466619,0.002207165,0.00002365479,0.0001053926,0.0002293588,0.00001895615,0.9575288,0.01782078,0.01546668,0.006341707,0.00005226508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.012012,0.0003139311,0.9792859,0.0001279203,0.00005177401,0.0001059605,0.0006537309,0.006911277,0.0005374685],"genre_scores_gemma":[0.1341527,0.0004197084,0.8585671,0.0003411792,0.00006605047,0.0005773358,0.002982345,0.001446265,0.001447258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002304648,"threshold_uncertainty_score":0.01074433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03628003819233797,"score_gpt":0.2814574738628821,"score_spread":0.2451774356705441,"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."}}