{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008580736,0.001296998,0.0006475954,0.0007142043,0.0002254838,0.0005624148,0.001786156,0.00113269,0.002089872],"category_scores_gemma":[0.001120296,0.0004275497,0.0007337476,0.0006251796,0.0003081159,0.0006977554,0.0009093172,0.001295249,0.0008064412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000754341,"about_ca_system_score_gemma":0.0008496272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004866798,"about_ca_topic_score_gemma":0.006923498,"domain_scores_codex":[0.9997657,0.00004816427,0.00001089615,0.00008506978,0.00005727345,0.00003291417],"domain_scores_gemma":[0.9997554,0.0001085191,0.00002136401,0.00002163443,0.00007026688,0.00002274133],"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":[0.0007674883,0.0004499994,0.01246815,0.0003086709,0.0006019375,0.0003457412,0.00006160314,0.5701345,0.01694013,0.002212282,0.02395597,0.3717537],"study_design_scores_gemma":[0.00002572129,0.00009040262,0.0007415739,0.00001449075,0.00003451408,0.00004024785,0.000005646904,0.9935493,0.002688726,0.001613955,0.001185513,0.000009913716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2051654,0.005952146,0.7545356,0.002086196,0.000444636,0.0002686716,0.005635452,0.01838258,0.007529185],"genre_scores_gemma":[0.7580226,0.001483058,0.216797,0.001817055,0.0001699388,0.0005427191,0.01019063,0.0003553859,0.0106217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004866798,"threshold_uncertainty_score":0.009676933,"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."}}