{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007990647,0.00005829668,0.0001327668,0.0001741542,0.00002752227,0.000004443957,0.00003737575,0.00004463895,0.00001930426],"category_scores_gemma":[0.00005255835,0.00004678368,0.00005922648,0.0002330514,0.00003164629,0.00004151403,0.00001770866,0.00007232737,0.000008966092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004537977,"about_ca_system_score_gemma":0.0001105956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005626049,"about_ca_topic_score_gemma":4.575649e-7,"domain_scores_codex":[0.9993981,0.000009519084,0.000275802,0.00003728114,0.0001867941,0.00009255965],"domain_scores_gemma":[0.9995746,0.0000178688,0.00005512303,0.0001538462,0.0001670726,0.00003148907],"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":[0.00471086,0.001849027,0.007437673,0.009288666,0.003638462,0.000038041,0.01895609,0.6455329,0.1075084,0.04168015,0.002673503,0.1566862],"study_design_scores_gemma":[0.002809781,0.0006735027,0.0001015844,0.0004744197,0.0002247765,0.000002460865,0.00176437,0.7551043,0.2366405,0.001245849,0.0008627499,0.000095667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7745622,0.0004383453,0.1974779,0.0002470492,0.00009192393,0.0004908585,0.00001006022,0.00002393134,0.02665776],"genre_scores_gemma":[0.9962957,0.00006607378,0.003158038,0.0001688832,0.000004807958,0.0000148248,0.0001006373,0.00000501627,0.0001860453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2217335,"threshold_uncertainty_score":0.1907783,"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."}}