{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06132177,0.001811095,0.00394115,0.004535008,0.004494924,0.02003726,0.006373733,0.01675911,0.008331404],"category_scores_gemma":[0.06860367,0.001439231,0.002237734,0.005801338,0.02438474,0.04403364,0.01131141,0.03127635,0.002906713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006703476,"about_ca_system_score_gemma":0.01382661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005051498,"about_ca_topic_score_gemma":0.004027368,"domain_scores_codex":[0.9742356,0.01639168,0.000944007,0.002012357,0.005338097,0.00107833],"domain_scores_gemma":[0.8303819,0.1431277,0.00256409,0.004617116,0.01142703,0.007882226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002166068,0.0002805433,0.002227589,0.003104639,0.0003086794,0.0004402286,0.001446734,0.004448793,0.0003667893,0.6791278,0.1775205,0.1305112],"study_design_scores_gemma":[0.00004120723,0.00004437046,0.000444978,0.00151301,0.00003549932,0.0002251917,0.001747898,0.003084391,0.0001110339,0.9112169,0.0814668,0.00006877765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.0009967636,0.1104234,0.01353919,0.8674995,0.00324467,0.00002944652,0.0002279803,0.00006954776,0.003969483],"genre_scores_gemma":[0.0911402,0.4985966,0.06671327,0.2683035,0.07048066,0.0005454134,0.000797152,0.0002448345,0.003178422],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.06132177,"threshold_uncertainty_score":0.3243043,"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."}}