{"id":"W4406677956","doi":"10.1186/s12859-024-06015-x","title":"A graph neural network approach for hierarchical mapping of breast cancer protein communities","year":2025,"lang":"en","type":"article","venue":"BMC 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 Winnipeg; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Breast cancer; Computational biology; Cluster analysis; Computer science; Hierarchical clustering; Machine learning; Artificial intelligence; Bioinformatics; Biology; Data mining; Cancer; 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":[0.0004480612,0.0007181464,0.0004680514,0.001354044,0.0004253772,0.0005380826,0.001026653,0.000924312,0.001407846],"category_scores_gemma":[0.001612422,0.0003452499,0.0008649539,0.0009716146,0.0004993463,0.0009373857,0.0007252535,0.001010982,0.000301054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216527,"about_ca_system_score_gemma":0.0007385957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01432723,"about_ca_topic_score_gemma":0.01669813,"domain_scores_codex":[0.9996912,0.00008104798,0.00001254486,0.0001121291,0.00005657392,0.00004652481],"domain_scores_gemma":[0.9994984,0.0002265014,0.00008186768,0.00003778488,0.0001175022,0.00003796512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008020453,0.00006580503,0.002339999,0.00007839262,0.00007949965,0.0001020411,0.00009765615,0.9092977,0.005005799,0.01201563,0.001944428,0.06889283],"study_design_scores_gemma":[0.000001203886,0.00000439834,0.0001218693,0.000001803046,0.000002739474,0.000005214169,0.000004409078,0.9958767,0.0001635674,0.003713045,0.0001031582,0.000001883973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04559343,0.0003886071,0.9508018,0.0003602921,0.00003765365,0.00005876031,0.0003713065,0.0006441136,0.001743926],"genre_scores_gemma":[0.6825538,0.000415228,0.3109577,0.0002892131,0.00007610632,0.000215579,0.001179874,0.0001386486,0.004173906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01432723,"threshold_uncertainty_score":0.02848768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749997163686651,"score_gpt":0.2430715725826016,"score_spread":0.2255716009457351,"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."}}