{"id":"W4412726776","doi":"10.1021/acs.jcim.5c01219","title":"protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"High Level Innovation and Entrepreneurial Research Team Program in Jiangsu; National Institutes of Health; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Missense mutation; PTEN; Phenotype; Computational biology; Genetics; Biology; Mutation; Cancer; Context (archaeology); Machine learning; PI3K/AKT/mTOR pathway; Computer science; Gene; Signal transduction","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":[0.00008648966,0.00004078722,0.00006702047,0.00005631811,0.00002825357,0.0000160529,0.00002877855,0.00003288607,6.928456e-7],"category_scores_gemma":[0.00004805707,0.00003709212,0.00002071089,0.00002524266,0.00001471335,0.00002775039,0.00001585599,0.00004680084,8.099082e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007458507,"about_ca_system_score_gemma":0.00004487529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009432688,"about_ca_topic_score_gemma":0.00000723633,"domain_scores_codex":[0.9996051,0.000005455403,0.0002739636,0.00004005654,0.00003090769,0.00004457501],"domain_scores_gemma":[0.9997596,0.000004476402,0.0001073943,0.00002444773,0.00008203658,0.00002211253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001706038,0.00003833089,0.00390629,0.0001069016,0.00002164051,5.99805e-8,0.0003659517,0.008953647,0.9722148,0.0001268179,0.000009644794,0.01408535],"study_design_scores_gemma":[0.001847768,0.0001326008,0.001923271,0.0001194134,0.00003773758,0.00001015197,0.0008749581,0.9136286,0.07972457,0.0006316101,0.0009295664,0.0001397903],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905199,0.0001868937,0.009054689,0.00008634662,0.00001543154,0.0000942983,0.000005988594,9.536801e-7,0.00003546599],"genre_scores_gemma":[0.9988915,0.0001510318,0.0008268494,0.00004858485,0.000007595248,0.000007486291,0.00005867721,0.000002066095,0.000006185931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9046749,"threshold_uncertainty_score":0.1512573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108228020142917,"score_gpt":0.2911804076363888,"score_spread":0.2803576056220971,"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."}}