{"id":"W4411690870","doi":"10.3390/computers14070253","title":"EMGP-Net: A Hybrid Deep Learning Architecture for Breast Cancer Gene Expression Prediction","year":2025,"lang":"en","type":"article","venue":"Computers","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Architecture; Breast cancer; Net (polyhedron); Expression (computer science); Deep learning; Gene; Computer science; Artificial intelligence; Computational biology; Computer architecture; Cancer; Oncology; Cancer research; Biology; Internal medicine; Medicine; Genetics; Mathematics; Geography; Programming language","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":[0.00005908245,0.0001134089,0.0000891108,0.00006321292,0.0001375991,0.00002696661,0.0001436196,0.00007508854,0.00001082763],"category_scores_gemma":[0.00001086429,0.0001049253,0.00007565731,0.00007372948,0.00002658376,0.000002879467,0.00007422201,0.00008133131,9.513806e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000245683,"about_ca_system_score_gemma":0.00005126627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003330265,"about_ca_topic_score_gemma":0.000002818887,"domain_scores_codex":[0.9992614,0.00004022727,0.0001306971,0.0003423062,0.00006957259,0.0001558083],"domain_scores_gemma":[0.9996061,0.000009227049,0.00006255275,0.0002029679,0.00006954958,0.00004964895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001463875,0.00002342115,0.001164436,0.00003327641,0.00002987658,2.572762e-7,0.00004397498,0.014078,0.8396951,0.00001260943,0.01921885,0.1255538],"study_design_scores_gemma":[0.001379857,0.0001202128,0.01559627,0.0001545098,0.00003222527,0.00001539376,0.00005257439,0.01557638,0.7357483,0.0001730342,0.2309105,0.0002407134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1099726,0.001034475,0.8868192,0.0007304214,0.0008592178,0.0002763863,0.00005180795,0.00004314222,0.0002126444],"genre_scores_gemma":[0.9922985,0.0002157163,0.005090286,0.0005705194,0.000468753,0.0001772195,0.000231309,0.0000176883,0.000929991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8823259,"threshold_uncertainty_score":0.4278728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005965486868621438,"score_gpt":0.2448635651187004,"score_spread":0.238898078250079,"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."}}