{"id":"W4254702297","doi":"10.1101/2021.02.16.431009","title":"PhylEx: Accurate reconstruction of clonal structure via integrated analysis of bulk DNA-seq and single cell RNA-seq data","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Vetenskapsrådet; Uppsala Universitet; Linköpings Universitet; Stiftelsen för Strategisk Forskning; Michael Smith Health Research BC","keywords":"Biology; RNA-Seq; Computational biology; clone (Java method); Cluster analysis; Tree (set theory); Gene; Genetics; Transcriptome; Computer science; Gene expression; Artificial intelligence; Mathematics","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":[0.000289377,0.0004931689,0.0008680999,0.0002979872,0.00007109304,0.00009511516,0.0006770998,0.0007920419,0.00005225803],"category_scores_gemma":[0.0001291908,0.0005184069,0.0002287769,0.0007090581,0.0002806766,0.00002262254,0.000608499,0.000441679,3.614223e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004868396,"about_ca_system_score_gemma":0.0004965604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002351867,"about_ca_topic_score_gemma":0.0000878223,"domain_scores_codex":[0.997303,0.0001580384,0.0007720595,0.001194321,0.0002595517,0.0003129938],"domain_scores_gemma":[0.9965704,0.00002605205,0.0007554705,0.001755707,0.0007376996,0.000154666],"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":[0.0001019927,0.0001608211,0.006227848,0.0003530883,0.00127869,0.000006137743,0.00001099042,0.0003512981,0.9914365,0.000004968766,0.00002648801,0.00004121084],"study_design_scores_gemma":[0.0004624653,0.0001026181,0.01074205,0.0001344498,0.001177517,9.310592e-8,0.00001292551,0.002853271,0.9837881,0.000001262921,0.0002339729,0.0004913231],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887052,0.001937682,0.005624903,0.00002110405,0.0006964208,0.0002433341,0.002736589,0.00002788651,0.000006935564],"genre_scores_gemma":[0.9917024,0.0005933963,0.007282467,0.0000402739,0.0001856413,0.000007683163,0.000116221,0.00006738264,0.000004500463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007648413,"threshold_uncertainty_score":0.9997268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836776823699153,"score_gpt":0.2168955277792092,"score_spread":0.1985277595422177,"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."}}