{"id":"W4412079431","doi":"10.1101/2025.07.06.25330974","title":"MOKA: A pipeline for multi-omics bridged SNP-set kernel association test","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Calgary","funders":"Alberta Innovates; Agriculture Funding Consortium; Mathison Centre for Mental Health Research and Education; Western Grains Research Foundation","keywords":"Pipeline (software); SNP; Test (biology); Set (abstract data type); Association (psychology); Kernel (algebra); Association test; Omics; Computational biology; Test set; Computer science; Data mining; Biology; Single-nucleotide polymorphism; Mathematics; Genetics; Psychology; Artificial intelligence; Gene; Operating system; Combinatorics; Genotype; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003150269,0.001831938,0.001537384,0.002815597,0.001094641,0.002081196,0.003185679,0.001458276,0.01700974],"category_scores_gemma":[0.01260417,0.001547473,0.002866856,0.001561662,0.0006727832,0.001170237,0.003287636,0.002956868,0.01164092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007789042,"about_ca_system_score_gemma":0.002552275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006555486,"about_ca_topic_score_gemma":0.009654807,"domain_scores_codex":[0.9983714,0.0004303822,0.0001764663,0.0004616902,0.0004118943,0.000148115],"domain_scores_gemma":[0.9966551,0.001912472,0.0002962019,0.000503024,0.0004551504,0.0001780386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003098956,0.0004209048,0.01760926,0.0022706,0.003297253,0.001634591,0.0006092364,0.09185935,0.0582735,0.02112575,0.2437288,0.5560718],"study_design_scores_gemma":[0.000667058,0.0001694678,0.01001928,0.0001831623,0.0003785968,0.0008963592,0.0001263039,0.8153113,0.03124689,0.06939238,0.07126029,0.0003489234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004671067,0.0004004249,0.8510748,0.0002783746,0.0001026762,0.0002159057,0.0055513,0.137039,0.0006664927],"genre_scores_gemma":[0.09318189,0.0003359324,0.8699232,0.0005472378,0.00007837305,0.001484908,0.0166668,0.01402133,0.003760224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01700974,"threshold_uncertainty_score":0.05690324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03887002255414844,"score_gpt":0.3404792409777644,"score_spread":0.301609218423616,"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."}}