{"id":"W4402029745","doi":"10.1101/2024.08.29.24312805","title":"Efficient molecular mendelian randomization screens with LaScaMolMR.jl","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut universitaire de cardiologie et de pneumologie de Québec","funders":"","keywords":"Mendelian randomization; Randomization; Mendelian inheritance; Genetics; Computational biology; Biology; Computer science; Gene; Bioinformatics; Genetic variants; Clinical trial; 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":[0.005430808,0.001155518,0.001919994,0.00104686,0.0005695384,0.001552948,0.002319592,0.0009601461,0.03030712],"category_scores_gemma":[0.01380342,0.0008629198,0.001999687,0.0007906063,0.0006674166,0.0007182156,0.00197735,0.001781388,0.01002381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005786559,"about_ca_system_score_gemma":0.001913991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001267202,"about_ca_topic_score_gemma":0.002297251,"domain_scores_codex":[0.9971842,0.001434446,0.0001172277,0.0003967129,0.000642368,0.0002249659],"domain_scores_gemma":[0.9937145,0.004491298,0.000383728,0.0008387256,0.0003768681,0.0001949409],"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":[0.004206146,0.0007737499,0.01537656,0.003233078,0.002109707,0.00180544,0.0005366294,0.1032787,0.08688538,0.1268537,0.2895387,0.3654022],"study_design_scores_gemma":[0.002479684,0.0004247311,0.004136219,0.0002868615,0.00044944,0.0007098795,0.00008861202,0.673244,0.05298618,0.1134416,0.151465,0.0002877858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01872933,0.0005912876,0.8214813,0.0007376336,0.0002332422,0.0002912128,0.01142537,0.1406253,0.005885307],"genre_scores_gemma":[0.109084,0.0004110835,0.8350611,0.001266934,0.0001298053,0.001545816,0.01275931,0.03313312,0.006608794],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03030712,"threshold_uncertainty_score":0.1013874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008701290490280001,"score_gpt":0.2506419272573698,"score_spread":0.2419406367670898,"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."}}