{"id":"W4410014280","doi":"10.1016/j.microb.2025.100368","title":"Integrative research: Current trends and considerations for biomarker discovery and precision medicine","year":2025,"lang":"en","type":"article","venue":"The Microbe","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Precision medicine; Biomarker discovery; Data science; Current (fluid); Biomarker; Medicine; Medical physics; Computer science; Biology; Engineering; Pathology; Proteomics","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":[0.0003027915,0.00006408586,0.0000743456,0.00005707526,0.0001674852,0.00004076294,0.0000477261,0.00003359835,0.00000466599],"category_scores_gemma":[0.0004496842,0.00003884389,0.0000181688,0.00006615039,0.0002800136,0.000002097818,0.0001367283,0.0000558971,2.051452e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008997339,"about_ca_system_score_gemma":0.00004824752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003674701,"about_ca_topic_score_gemma":0.0002633731,"domain_scores_codex":[0.9995645,0.00004504748,0.00009374405,0.000167991,0.00003142783,0.00009730977],"domain_scores_gemma":[0.9993865,0.0003451888,0.00001880743,0.0001475916,0.00008080507,0.00002103305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001502122,0.00003055528,0.0001909035,0.00002174816,0.00005140257,2.535893e-7,0.0004361118,0.000001815657,0.6420248,0.009972717,0.2589892,0.0881303],"study_design_scores_gemma":[0.002093574,0.0005082848,0.006844703,0.0002275767,0.00009050297,0.00001412144,0.0007869137,0.0002096385,0.1414922,0.04048694,0.8070366,0.0002088417],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8884185,0.06632163,0.01386319,0.02507767,0.0006914097,0.0008011675,0.0003781942,0.000008084702,0.004440137],"genre_scores_gemma":[0.9926664,0.004206277,0.0002581987,0.0003495455,0.0001078885,0.00004268647,0.00006031147,0.000006259448,0.002302484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5480474,"threshold_uncertainty_score":0.1584008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05648689499976171,"score_gpt":0.3801002445347357,"score_spread":0.3236133495349739,"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."}}