{"id":"W4385761397","doi":"10.7554/elife.87133.2","title":"GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Inference; Omics; Computer science; Genomics; Computational biology; Genome; Transformation (genetics); Spatial analysis; 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":[0.001626851,0.000912359,0.0006661212,0.001223201,0.0003317141,0.001544112,0.001570872,0.0007082371,0.003310759],"category_scores_gemma":[0.003500354,0.0005150862,0.002090812,0.001340893,0.000917435,0.001207908,0.002537706,0.001981533,0.001501183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007644304,"about_ca_system_score_gemma":0.001090072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002987713,"about_ca_topic_score_gemma":0.003738852,"domain_scores_codex":[0.999123,0.0002587872,0.00004786626,0.0002257268,0.0002828817,0.0000617207],"domain_scores_gemma":[0.9988033,0.0004538845,0.0001367151,0.0003587929,0.000181922,0.00006540572],"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":[0.0008625699,0.0002177987,0.007214755,0.0005741869,0.0004691749,0.000900049,0.0005810763,0.3972074,0.06659491,0.1177023,0.04113382,0.366542],"study_design_scores_gemma":[0.0000391759,0.00007145353,0.001163075,0.00003545653,0.00003260269,0.000223183,0.00009052933,0.8887663,0.02264699,0.06429092,0.0225878,0.0000525311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003637029,0.00006978415,0.9861005,0.0001910537,0.00003919826,0.00002962482,0.0009195292,0.008625295,0.0003879786],"genre_scores_gemma":[0.1074267,0.0002643823,0.8819426,0.0003738411,0.00005397842,0.0002343991,0.005461211,0.00257385,0.001669053],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003310759,"threshold_uncertainty_score":0.01107562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05300175544226658,"score_gpt":0.3145824333051392,"score_spread":0.2615806778628726,"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."}}