{"id":"W4392747409","doi":"10.1101/2024.03.10.584320","title":"OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unavailability; Phenotype; Benchmarking; Computational biology; Genetic architecture; Transcriptome; Computer science; Biology; Data mining; Genetics; Statistics; Gene; Mathematics; Gene expression","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":[0.001076184,0.001030862,0.0005812387,0.0005897825,0.0003635883,0.000887303,0.001952632,0.001212985,0.01482506],"category_scores_gemma":[0.005899978,0.0004910114,0.001171071,0.0004081213,0.0004786029,0.000842717,0.001384174,0.00143301,0.001940957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006180842,"about_ca_system_score_gemma":0.001380461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007323101,"about_ca_topic_score_gemma":0.006559435,"domain_scores_codex":[0.9997208,0.00008979471,0.00002316795,0.00006223986,0.00006885075,0.00003516485],"domain_scores_gemma":[0.9981945,0.001260253,0.00008484226,0.0001617185,0.0002017876,0.000096921],"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":[0.0003489872,0.0001177636,0.009636631,0.0003360653,0.0001206616,0.0003273142,0.0001557551,0.9299411,0.002949044,0.02054022,0.01268346,0.02284291],"study_design_scores_gemma":[0.00005333346,0.00002196339,0.0002575188,0.00001615879,0.0000141633,0.00003013743,0.0000142398,0.9869083,0.001157381,0.006840724,0.004675346,0.000010787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07205256,0.0004832558,0.8486174,0.001055406,0.000372251,0.0003021851,0.01339539,0.05244981,0.01127168],"genre_scores_gemma":[0.653357,0.0006653539,0.3077718,0.001082211,0.0000845189,0.001325611,0.01870635,0.006992491,0.01001462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01482506,"threshold_uncertainty_score":0.04959482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576518156765828,"score_gpt":0.2599717563990919,"score_spread":0.2442065748314337,"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."}}