{"id":"W4409628358","doi":"10.1158/1538-7445.am2025-3913","title":"Abstract 3913: New insights from transgenic mouse models of PyMT-induced breast cancer: identifying novel long non-coding RNA biomarkers","year":2025,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill Genome Centre; McGill University Health Centre","funders":"","keywords":"Breast cancer; Genetically modified mouse; Computational biology; Cancer; Transgene; Biology; RNA; Cancer research; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002980397,0.0005487716,0.000410671,0.001021772,0.0001994423,0.0004091688,0.00032161,0.0004401798,0.002706797],"category_scores_gemma":[0.0001786494,0.0002255763,0.000516447,0.0005103992,0.0003557708,0.0003428722,0.0002839524,0.000985058,0.0008042955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003516492,"about_ca_system_score_gemma":0.0003057439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005328958,"about_ca_topic_score_gemma":0.0009388273,"domain_scores_codex":[0.9997397,0.00002392666,0.00002305171,0.00007960136,0.00009060242,0.00004318043],"domain_scores_gemma":[0.9998404,0.00001994637,0.00006511987,0.000015821,0.00001986535,0.00003889104],"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":[0.00009294352,0.00002578385,0.0004248381,0.00004707593,0.000009166147,0.00008372247,0.000009802211,0.00006213151,0.9974163,0.0001120764,0.000114959,0.001601248],"study_design_scores_gemma":[0.00008646178,0.0007654535,0.03437517,0.00004378027,0.0001283298,0.001981833,0.000103695,0.003906053,0.9437935,0.0007527333,0.01402904,0.00003385778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9385092,0.003829478,0.04222767,0.0008484546,0.0001430201,0.0001516572,0.01128839,0.0009252616,0.002076976],"genre_scores_gemma":[0.9477808,0.004472838,0.02474856,0.0004332799,0.00006056015,0.0004011164,0.01264219,0.0004052413,0.009055307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002706797,"threshold_uncertainty_score":0.009055138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06742473756808541,"score_gpt":0.3777631622211927,"score_spread":0.3103384246531073,"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."}}