{"id":"W4405641024","doi":"10.1101/2024.12.13.24318992","title":"An integrative analysis of consortium-based multi-omics QTL and genome-wide association study data uncovers new biomarkers for lung cancer","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Ferroptosis and cancer prognosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"National Institutes of Health; Priority Academic Program Development of Jiangsu Higher Education Institutions; Nanjing Medical University; National Natural Science Foundation of China","keywords":"Lung cancer; Computational biology; Genome-wide association study; Quantitative trait locus; Omics; Biology; Data science; Bioinformatics; Computer science; Medicine; Genetics; Oncology; Single-nucleotide polymorphism; Gene; Genotype","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":[0.005851095,0.001014374,0.00139534,0.005903388,0.0008730681,0.002428937,0.0007519438,0.000562045,0.004419077],"category_scores_gemma":[0.006764761,0.0005838136,0.003652435,0.006296152,0.0003043329,0.0007903184,0.002911413,0.000795393,0.0008706806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006306121,"about_ca_system_score_gemma":0.00237661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00510373,"about_ca_topic_score_gemma":0.008369412,"domain_scores_codex":[0.9969975,0.0008311335,0.0002489839,0.0009980029,0.000653298,0.0002710641],"domain_scores_gemma":[0.9952909,0.002207568,0.0006836525,0.0006334781,0.0007429079,0.000441642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002692167,0.0003014604,0.4784341,0.003720565,0.01378827,0.002368013,0.00146262,0.01594398,0.2831358,0.006228796,0.0195647,0.1723595],"study_design_scores_gemma":[0.0004504883,0.0006571803,0.7913864,0.0005103027,0.01035793,0.001338653,0.001167048,0.06306812,0.03962033,0.015279,0.07570577,0.0004587149],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5877469,0.00785224,0.2259718,0.002484076,0.0004117121,0.0006506573,0.1604416,0.008031109,0.006409991],"genre_scores_gemma":[0.671522,0.001971725,0.2135914,0.0008093583,0.000187253,0.0008134071,0.106117,0.00236807,0.002619927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005903388,"threshold_uncertainty_score":0.03094387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04936100860890553,"score_gpt":0.3699076317641442,"score_spread":0.3205466231552387,"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."}}