{"id":"W4408912460","doi":"10.1101/2025.03.25.25324634","title":"<i>3D IntelliGenes:</i> AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"","keywords":"Biomarker discovery; Visualization; Computer science; Biomarker; Omics; Computational biology; Data mining; Bioinformatics; Proteomics; Biology","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":[0.002736402,0.002587175,0.001205239,0.00250721,0.0006680167,0.003360851,0.002362705,0.001538978,0.04522895],"category_scores_gemma":[0.007259227,0.000959368,0.002429312,0.001172039,0.0007011757,0.001740096,0.003306155,0.001969664,0.008127272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005857082,"about_ca_system_score_gemma":0.0009828517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002240432,"about_ca_topic_score_gemma":0.002007589,"domain_scores_codex":[0.999166,0.000166678,0.00009986314,0.0001799406,0.000331847,0.00005572662],"domain_scores_gemma":[0.9967197,0.002071801,0.000201177,0.0003908974,0.0004135986,0.000202851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003330451,0.0005970213,0.01309824,0.003989705,0.001465508,0.002797593,0.002594065,0.06653459,0.06935733,0.02818825,0.4474437,0.3606035],"study_design_scores_gemma":[0.0008142072,0.0003610118,0.009365035,0.0006562542,0.0002476269,0.001485578,0.0002797382,0.6400681,0.1036758,0.03096882,0.2113986,0.0006793526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01195282,0.0003767881,0.4876641,0.0009734384,0.0003010415,0.0004241317,0.0223963,0.4704337,0.00547773],"genre_scores_gemma":[0.1382466,0.001020918,0.7493405,0.001756175,0.0002815015,0.002166322,0.04102957,0.05705259,0.009105723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04522895,"threshold_uncertainty_score":0.1513059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530813763838524,"score_gpt":0.3133341596713476,"score_spread":0.2780260220329623,"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."}}