{"id":"W4378837697","doi":"10.7554/elife.87133.1","title":"GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Novo Nordisk Fonden; H. Lundbeck A/S; Lundbeckfonden; Aarhus Universitet; Novo Nordisk; Aarhus Universitets Forskningsfond","keywords":"Inference; Omics; Computer science; Genomics; Computational biology; Spatial analysis; Genome; Transformation (genetics); Data mining; Data science; Bioinformatics; Artificial intelligence; Biology; Gene; Genetics; Mathematics","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":[0.001527209,0.0009121695,0.0006569176,0.001157063,0.000321622,0.00148749,0.00155239,0.0006887134,0.003225242],"category_scores_gemma":[0.003287622,0.0005064744,0.002032815,0.001295889,0.0009079276,0.00113566,0.002365085,0.001962974,0.001499399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007631091,"about_ca_system_score_gemma":0.001043742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002889489,"about_ca_topic_score_gemma":0.003586696,"domain_scores_codex":[0.9991679,0.0002377319,0.00004529804,0.0002192553,0.0002710606,0.00005872482],"domain_scores_gemma":[0.998888,0.0004268893,0.0001279333,0.0003275661,0.0001697351,0.00006006759],"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":[0.0008663271,0.0002164719,0.007201064,0.0005937468,0.0004511942,0.0009171029,0.00059043,0.3954428,0.07900523,0.1173857,0.04019369,0.3571362],"study_design_scores_gemma":[0.00003909375,0.00007340215,0.001220388,0.00003593755,0.00003353788,0.000224003,0.00009205927,0.8834937,0.02634516,0.0640156,0.02437338,0.00005378565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003767123,0.00007143096,0.9856948,0.0001922633,0.00004040193,0.00002955936,0.0009494745,0.008861701,0.0003932155],"genre_scores_gemma":[0.1119422,0.0002786782,0.8768733,0.0003756708,0.000054521,0.0002549026,0.005760711,0.002695002,0.001765144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003225242,"threshold_uncertainty_score":0.01078951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09056053654883003,"score_gpt":0.3427477981053011,"score_spread":0.2521872615564711,"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."}}