{"id":"W2900604024","doi":"10.2174/1381612824666181112114228","title":"Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases","year":2018,"lang":"en","type":"review","venue":"Current Pharmaceutical Design","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Epigenetics; Precision medicine; Artificial intelligence; Machine learning; Computer science; Big data; Deep learning; Data science; Bioinformatics; Medicine; Biology; Data mining; Gene; Genetics; Pathology","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":[0.002213655,0.0006010791,0.001135164,0.0002449275,0.0001189235,0.00003499502,0.0004495894,0.0003807669,0.0001745677],"category_scores_gemma":[0.00203486,0.0004742422,0.0003297413,0.0003925036,0.0002091608,0.00000479612,0.0002902492,0.0006903993,0.00004663414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006631371,"about_ca_system_score_gemma":0.0001841366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002399019,"about_ca_topic_score_gemma":3.922818e-7,"domain_scores_codex":[0.9939724,0.003126671,0.001072051,0.0009715697,0.0003064124,0.000550855],"domain_scores_gemma":[0.998234,0.0005709737,0.0003613419,0.0003883045,0.0001136683,0.0003316991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003524211,0.000104135,0.0000562185,0.002360755,0.00005378702,0.000003353861,0.00001377544,0.00004512144,0.001205632,0.00000915407,0.0003722307,0.9957406],"study_design_scores_gemma":[0.0004538769,0.0003653593,0.000008804373,0.002963909,0.0005405269,0.000002474821,0.000002136596,0.002036823,0.001704335,0.0002077023,0.9912109,0.0005031182],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004767796,0.8179452,0.1802327,0.00001071955,0.0008574588,0.0008065805,0.0000184236,0.00002232096,0.0001018616],"genre_scores_gemma":[0.00006165835,0.9892743,0.008177545,0.00001451276,0.001241096,0.0001328777,0.0008943487,0.00009839705,0.0001052344],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9952375,"threshold_uncertainty_score":0.9997709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2052753347205047,"score_gpt":0.5133061760988132,"score_spread":0.3080308413783085,"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."}}