{"id":"W4409979195","doi":"10.1002/minf.202500018","title":"Deep Modeling of Gain‐of‐Function Mutations on Androgen Receptor","year":2025,"lang":"en","type":"article","venue":"Molecular Informatics","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Androgen receptor; Prostate cancer; Mutant; Computational biology; Mutation; Antiandrogens; Function (biology); Computer science; Biology; Bioinformatics; Genetics; Gene; Cancer","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":[0.00023882,0.0006666823,0.0005751131,0.000246717,0.0001269574,0.0004765043,0.0005804675,0.0006740685,0.001079586],"category_scores_gemma":[0.0006106895,0.0002936317,0.000735481,0.0002394404,0.0003089772,0.0003338296,0.0003416251,0.0007366514,0.0001620171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004302024,"about_ca_system_score_gemma":0.0006734077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005476729,"about_ca_topic_score_gemma":0.005392646,"domain_scores_codex":[0.9999259,0.00002107403,0.000003435486,0.00001658283,0.00001401169,0.00001892353],"domain_scores_gemma":[0.9998542,0.00009222848,0.00001782622,0.000008453153,0.00001492479,0.00001227938],"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":[0.00004260582,0.00002548202,0.0005862356,0.00002312812,0.00002482958,0.00003711945,0.000005583452,0.9912711,0.001978329,0.0009114319,0.0003427904,0.004751308],"study_design_scores_gemma":[0.000001681838,0.000009116501,0.0000469034,7.28343e-7,0.00000258866,0.000003270994,0.000001023128,0.999171,0.0003308056,0.0003548134,0.00007709213,8.824019e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5356314,0.001839509,0.4527833,0.0009050259,0.000129588,0.00005388206,0.001065252,0.001229192,0.006362908],"genre_scores_gemma":[0.9745213,0.0005134476,0.02146544,0.0001802171,0.00002316265,0.00006750362,0.0005550015,0.00005528543,0.002618764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005476729,"threshold_uncertainty_score":0.01088971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792586232352345,"score_gpt":0.3033978119549671,"score_spread":0.2854719496314437,"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."}}