{"id":"W4319944226","doi":"10.1101/2023.02.09.525144","title":"GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Novo Nordisk Fonden; H. Lundbeck A/S; Lundbeckfonden; Aarhus Universitet; Novo Nordisk; Aarhus Universitets Forskningsfond","keywords":"Inference; Omics; Computer science; Computational biology; Genome; Transformation (genetics); Genomics; 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":[0.001699255,0.0009476236,0.0006875245,0.001191398,0.0003330977,0.001611512,0.001632862,0.0007476181,0.003634646],"category_scores_gemma":[0.003161588,0.0005474315,0.001968057,0.001205699,0.0008643088,0.001123994,0.002359314,0.002041854,0.001656584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007580579,"about_ca_system_score_gemma":0.001069428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002698261,"about_ca_topic_score_gemma":0.003553838,"domain_scores_codex":[0.9992251,0.0002218506,0.00004252177,0.0001949978,0.0002588156,0.00005666058],"domain_scores_gemma":[0.9989992,0.0003747667,0.0001208785,0.0003008645,0.0001456918,0.00005865144],"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":[0.001057192,0.0002547387,0.007637407,0.0007552208,0.0006024466,0.0009385453,0.0006395183,0.3563215,0.092883,0.1152106,0.0638824,0.3598174],"study_design_scores_gemma":[0.00005588678,0.00008068029,0.001401478,0.00004469147,0.00003883233,0.0002415809,0.00009859971,0.875526,0.03123373,0.06007917,0.03113252,0.00006684443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004301282,0.0000921888,0.9789855,0.0002503396,0.00005385485,0.0000367873,0.001474013,0.01431836,0.0004876769],"genre_scores_gemma":[0.1019162,0.0003402643,0.884011,0.0004459288,0.00005948947,0.0002960844,0.0072664,0.003848683,0.001815972],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003634646,"threshold_uncertainty_score":0.01215905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02877062595442288,"score_gpt":0.2582655061450734,"score_spread":0.2294948801906506,"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."}}