{"id":"W1975032412","doi":"10.3389/fgene.2014.00015","title":"The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Standortagentur Tirol; Queen's University; Queen's University Belfast","keywords":"Breast cancer; Gene regulatory network; Cancer; Gene; Biology; Regulator gene; Computational biology; Regulation of gene expression; Genetics; Cancer research; Medicine; Gene expression","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":[],"consensus_categories":[],"category_scores_codex":[0.0004851256,0.0001938783,0.0002519544,0.00003511307,0.0001235946,0.00002227921,0.0003735719,0.0002587617,0.00000641492],"category_scores_gemma":[0.00001373822,0.0001538915,0.0001110922,0.0001246914,0.0001776781,0.000002644422,0.00009084426,0.0001197077,2.641676e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000342526,"about_ca_system_score_gemma":0.0001516422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002830866,"about_ca_topic_score_gemma":0.0003932476,"domain_scores_codex":[0.9987302,0.00005603062,0.0004309647,0.0002474044,0.0001244935,0.0004109014],"domain_scores_gemma":[0.9990134,0.00002071685,0.0002268757,0.0005190054,0.0001429766,0.00007701061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005938528,0.00004273591,0.1013599,0.00008243055,0.0003324037,2.74906e-7,0.0001055456,0.05612366,0.00860422,0.000334357,0.3184682,0.5139525],"study_design_scores_gemma":[0.003993997,0.0004578971,0.2072685,0.0002638346,0.0002315643,0.00001244848,0.0004190132,0.2497632,0.03276524,0.004363929,0.4992404,0.001220018],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7431671,0.1034891,0.1430508,0.0008082706,0.006549541,0.001370461,0.000664742,0.00002755546,0.000872437],"genre_scores_gemma":[0.958279,0.01151209,0.02694932,0.0004258387,0.001536585,0.0001706776,0.000172258,0.00006775755,0.0008864865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5127325,"threshold_uncertainty_score":0.6275514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003447243310466374,"score_gpt":0.2086769729212066,"score_spread":0.2052297296107402,"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."}}