{"id":"W4412570869","doi":"10.61340/fbdtpm","title":"From big data to personalized medicine: bioinformatics perspectives and challenges","year":2025,"lang":"en","type":"article","venue":"ScienceBank","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Personalized medicine; Big data; Data science; Translational bioinformatics; Computer science; Bioinformatics; Medicine; Data mining; Genomics; Biology; Genetics","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":[0.0007740017,0.0001327862,0.0001604194,0.0001512358,0.0001591816,0.00005719873,0.0008747032,0.0001003355,0.00002871699],"category_scores_gemma":[0.001263454,0.00009788328,0.00002271878,0.0002588813,0.0009963709,0.00001089724,0.0009333204,0.00008407535,0.00001944433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001367847,"about_ca_system_score_gemma":0.0002033768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006882341,"about_ca_topic_score_gemma":0.00006737582,"domain_scores_codex":[0.9986058,0.00003188425,0.0002327151,0.0004188386,0.0003838664,0.0003268593],"domain_scores_gemma":[0.99885,0.00004816178,0.00004129968,0.0007159233,0.0001293015,0.0002153366],"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":[0.0001027073,0.00008516777,0.0003254209,0.0002337468,0.0001060684,0.000002320138,0.01115738,0.000001162806,0.0791081,0.001030056,0.05235165,0.8554962],"study_design_scores_gemma":[0.001198665,0.000592248,0.006232617,0.0001494868,0.00003273042,0.000005015333,0.04875474,0.002037556,0.006609857,0.001149724,0.9328721,0.0003652955],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5290175,0.1339995,0.0422543,0.1795675,0.004580618,0.002298642,0.0008853379,0.000139224,0.1072574],"genre_scores_gemma":[0.8314458,0.1053254,0.04544323,0.006288026,0.002818497,0.00004700188,0.0006528717,0.000032655,0.007946513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8805204,"threshold_uncertainty_score":0.3991565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.089784149992143,"score_gpt":0.3464414346519495,"score_spread":0.2566572846598065,"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."}}