{"id":"W4412615070","doi":"10.1186/s13059-025-03682-8","title":"Joint representation and visualization of derailed cell states with Decipher","year":2025,"lang":"en","type":"article","venue":"Genome biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"National Cancer Institute; Fonds de Recherche du Québec - Santé; Marie-Josée and Henry R. Kravis Center for Molecular Oncology; Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center; Chan Zuckerberg Initiative; Howard Hughes Medical Institute","keywords":"DECIPHER; Biology; Visualization; Human genetics; Genome Biology; Computational biology; Evolutionary biology; Representation (politics); Joint (building); Genome; Bioinformatics; Genetics; Genomics; Computer science; Artificial intelligence; Engineering; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.00005247489,0.00007015582,0.0001083348,0.00004031456,0.00002878327,0.000005425514,0.00004278726,0.00008425725,0.00001119748],"category_scores_gemma":[0.0000108695,0.00005764913,0.00002003035,0.0000675526,0.00008025221,0.00000140513,0.00002592237,0.00002085054,6.908624e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004461931,"about_ca_system_score_gemma":0.0000306501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000388807,"about_ca_topic_score_gemma":0.00004372278,"domain_scores_codex":[0.9995176,0.00004162534,0.0001353728,0.0001924826,0.00002080196,0.00009209883],"domain_scores_gemma":[0.9997441,0.000006760143,0.00005069436,0.0001168626,0.00006276285,0.00001883932],"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":[0.0001141351,0.00003343766,0.026703,0.00002946263,0.00003033099,2.845368e-7,0.00007223112,0.00005756032,0.9719278,0.000160948,0.00004565609,0.0008251891],"study_design_scores_gemma":[0.001213116,0.0007689057,0.02927944,0.00001243824,0.00003418255,0.000003168695,0.0001349314,0.0001214822,0.9600157,0.0004753925,0.007793541,0.0001476958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632758,0.001102649,0.03443284,0.00004656832,0.00006302417,0.0001111327,0.000009725667,0.00000557234,0.0009526463],"genre_scores_gemma":[0.9979937,0.0004140823,0.0007649728,0.0001238196,0.00002531511,0.000007171889,0.0002524896,0.000006718018,0.0004117037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03471788,"threshold_uncertainty_score":0.2350863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055773315096001,"score_gpt":0.2520563304314073,"score_spread":0.2414985972804473,"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."}}