{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003819612,0.000271914,0.0003167429,0.0001309614,0.0002047628,0.0001351385,0.0001817966,0.0002335858,0.00006664326],"category_scores_gemma":[0.000184071,0.0002585508,0.0003621642,0.0004163363,0.00006561232,0.00002863419,0.0001612824,0.0001457135,0.00003683841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006002478,"about_ca_system_score_gemma":0.0003680451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005455143,"about_ca_topic_score_gemma":0.00008336281,"domain_scores_codex":[0.9983052,0.00004179468,0.0007864907,0.0002692213,0.0002265051,0.0003707674],"domain_scores_gemma":[0.998472,0.00003399572,0.0003982532,0.0005103476,0.0004577549,0.0001276226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004261479,0.002157884,0.005907053,0.004969857,0.01718793,0.00001498326,0.004354237,0.2336761,0.5449179,0.09453704,0.07231724,0.01569825],"study_design_scores_gemma":[0.003506997,0.001055988,0.0008697254,0.0001238722,0.0006756656,0.00005826132,0.003339622,0.7902842,0.1343141,0.002229173,0.06222036,0.001322061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06108276,0.0001654301,0.9351912,0.0001420665,0.0002034378,0.001218238,0.0001267147,0.00002532523,0.001844901],"genre_scores_gemma":[0.1193143,0.00003030028,0.8730761,0.0005720975,0.0003168433,0.0007011785,0.002594935,0.00003686832,0.003357421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5566081,"threshold_uncertainty_score":0.9999866,"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."}}