{"id":"W4388933875","doi":"10.1093/nar/gkad1027","title":"PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful","year":2023,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Arthritis Society; Discovery Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Pathway analysis; Biology; Biological pathway; Gene ontology; Computational biology; Annotation; Ontology; Computer science; Gene; Bioinformatics; Genetics","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":[0.009328618,0.002321239,0.002172033,0.007847179,0.001424273,0.005068761,0.003096555,0.001146919,0.03564938],"category_scores_gemma":[0.0239977,0.001722852,0.003036644,0.007706527,0.0006724926,0.005091903,0.007371646,0.003224721,0.0113263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348383,"about_ca_system_score_gemma":0.003651276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003901232,"about_ca_topic_score_gemma":0.006458776,"domain_scores_codex":[0.9942786,0.001704754,0.000618439,0.001379018,0.001674023,0.0003451368],"domain_scores_gemma":[0.9877211,0.007379523,0.0006027465,0.002071735,0.001859398,0.0003654676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004183966,0.0005714513,0.03106799,0.009053139,0.002330442,0.0009390002,0.001787925,0.01540951,0.0545664,0.02894885,0.5213776,0.3297637],"study_design_scores_gemma":[0.001533537,0.0005070437,0.02659823,0.001191544,0.001324499,0.001545957,0.0008393371,0.136675,0.0822219,0.08605928,0.6609855,0.0005181999],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02817491,0.001591118,0.4575678,0.001469081,0.0004506339,0.0006418495,0.2262663,0.2733043,0.01053394],"genre_scores_gemma":[0.06902791,0.001119072,0.5929266,0.0008857315,0.0001168262,0.001852003,0.293242,0.0373596,0.003470262],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03564938,"threshold_uncertainty_score":0.1192591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054193854640802,"score_gpt":0.3267379427432383,"score_spread":0.2961960041968302,"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."}}