{"id":"W4414382695","doi":"10.1016/j.crmeth.2025.101183","title":"A statistical physics approach to integrating multi-omics data for disease-module detection","year":2025,"lang":"en","type":"article","venue":"Cell Reports Methods","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Heart, Lung, and Blood Institute; National Institute on Aging; National Institutes of Health","keywords":"Interactome; Leverage (statistics); Epigenome; Human disease; DNA methylation; Complex disease; Systems biology; Human 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":[0.002613425,0.0007461281,0.0007590508,0.003223644,0.0005624329,0.000836009,0.001328359,0.0009376308,0.001007165],"category_scores_gemma":[0.006177631,0.0003841877,0.002093214,0.001700543,0.0008900715,0.001281392,0.001450484,0.001133197,0.0002682191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000853473,"about_ca_system_score_gemma":0.001571906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003882805,"about_ca_topic_score_gemma":0.005001523,"domain_scores_codex":[0.9991261,0.0003872698,0.00004768971,0.000184544,0.0002018028,0.00005268796],"domain_scores_gemma":[0.9975396,0.001674771,0.0002474888,0.0002697516,0.0001636209,0.0001048132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000963231,0.0002220033,0.01335065,0.0003407829,0.0007895413,0.0005232696,0.0002408823,0.5827202,0.02087796,0.2276656,0.002974507,0.1501983],"study_design_scores_gemma":[0.000007747932,0.00003032946,0.001437341,0.00000736382,0.00002630828,0.0000897156,0.0000148477,0.9209559,0.0009604727,0.07522698,0.001221574,0.00002144146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007135912,0.0001214073,0.9917533,0.0002016611,0.00002250294,0.00004003691,0.0001040374,0.0002206792,0.0004003758],"genre_scores_gemma":[0.3735156,0.0005097022,0.6223466,0.0004505976,0.0001975317,0.0003466806,0.0005985667,0.0001504032,0.001884351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003882805,"threshold_uncertainty_score":0.0138213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04005246912731791,"score_gpt":0.3660593315875215,"score_spread":0.3260068624602036,"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."}}