{"id":"W4388692040","doi":"10.1101/2023.11.11.566719","title":"Joint representation and visualization of derailed cell states with Decipher","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Marie-Josée and Henry R. Kravis Center for Molecular Oncology; National Cancer Institute; National Institutes of Health; Memorial Sloan-Kettering Cancer Center; Howard Hughes Medical Institute","keywords":"DECIPHER; Computational biology; Cell; Computer science; Biology; Visualization; Myeloid leukemia; Genetics; Artificial intelligence; Cancer research","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001962114,0.0002798518,0.0003056495,0.000103661,0.000059568,0.00006600089,0.0001421002,0.0003131675,0.000006662555],"category_scores_gemma":[0.00005671629,0.0002737059,0.00006748759,0.0001763628,0.0001013574,0.000006812979,0.0001577651,0.0001406774,0.000003569741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002287318,"about_ca_system_score_gemma":0.0001754081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008878298,"about_ca_topic_score_gemma":0.00001776396,"domain_scores_codex":[0.9985217,0.00007861012,0.0003506596,0.0006422414,0.0001892865,0.0002174353],"domain_scores_gemma":[0.9987288,0.0000131471,0.0002826759,0.0005340712,0.0003426733,0.0000986019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008576937,0.00008019892,0.02065742,0.0003673827,0.00009157177,0.00000636777,0.00001764541,0.0004591163,0.9780403,0.00002123449,0.0001713868,0.000001636721],"study_design_scores_gemma":[0.0006106631,0.0001887856,0.03993667,0.0001669273,0.00007801285,1.315525e-8,0.000008176612,0.0005049556,0.9578446,0.000002404078,0.0003197407,0.0003391001],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824414,0.0006147774,0.01608555,0.0000375937,0.0002820043,0.0003809699,0.00008618585,0.00006475449,0.000006763559],"genre_scores_gemma":[0.9959194,0.0009840338,0.002718825,0.0000472584,0.0001413776,0.00005323306,0.00000798881,0.0001058583,0.00002201671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02019572,"threshold_uncertainty_score":0.9999715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999686425117061,"score_gpt":0.2317805279822401,"score_spread":0.2117836637310695,"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."}}