{"id":"W2231730507","doi":"10.1242/dmm.023143","title":"Use of a genetically engineered mouse model as a preclinical tool for HER2 breast cancer","year":2015,"lang":"en","type":"article","venue":"Disease Models & Mechanisms","topic":"HER2/EGFR in Cancer Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Medical Research Council; Indian Council of Medical Research; World Cancer Research Fund; Cancer Research UK; AstraZeneca","keywords":"Genetically engineered; Breast cancer; Genetically modified organism; Cancer; Computational biology; Biology; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004941274,0.0002987028,0.0005684574,0.0001352257,0.00004055857,0.00004279175,0.000268489,0.0001883077,0.0001214318],"category_scores_gemma":[0.0004925339,0.0002706237,0.0003046391,0.0001732663,0.00009028594,0.0002037181,0.0001457694,0.0002412971,0.00001871433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002494952,"about_ca_system_score_gemma":0.00266707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001889249,"about_ca_topic_score_gemma":0.00001275591,"domain_scores_codex":[0.9969616,0.00007180199,0.000578502,0.0006289231,0.001127958,0.0006312124],"domain_scores_gemma":[0.996515,0.0001269271,0.00008823513,0.0008535777,0.0009654816,0.001450781],"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":[0.029501,0.004281707,0.0004420532,0.002257026,0.0009384722,0.0001548467,0.00132847,0.7356955,0.0762851,0.1133442,0.0119971,0.02377451],"study_design_scores_gemma":[0.002779784,0.0004028761,0.00006431533,0.0001847833,0.0003164353,0.000008583825,0.00002264084,0.9432552,0.00768123,0.04482672,0.0001722681,0.000285192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3913551,0.000390299,0.6000897,0.002468655,0.0001511574,0.002619657,0.002686054,0.0001610231,0.00007829882],"genre_scores_gemma":[0.9466248,0.0001561984,0.04803345,0.00112142,0.0001813685,0.001354364,0.00009669217,0.0001460872,0.002285587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5552697,"threshold_uncertainty_score":0.9999746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1813091199927425,"score_gpt":0.4036788950936382,"score_spread":0.2223697751008957,"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."}}