{"id":"W3123488809","doi":"10.1101/2021.01.15.426865","title":"DNA-based copy number analysis confirms genomic evolution of PDX models","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Congressionally Directed Medical Research Programs; Office of Research Infrastructure Programs, National Institutes of Health; National Cancer Institute; National Institutes of Health; V Foundation for Cancer Research; Israel Cancer Research Fund; Azrieli Foundation; Israel Cancer Association","keywords":"Biology; Genome; Genetics; Genome instability; Somatic evolution in cancer; genomic DNA; Cancer; Phenotype; Copy-number variation; DNA; Computational biology; Evolutionary biology; Gene; DNA damage","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005466773,0.0001993927,0.0003579417,0.000798545,0.0002082269,0.0007386756,0.0003146512,0.0003669841,0.001796192],"category_scores_gemma":[0.002081908,0.0001863396,0.0002717649,0.0007123805,0.00033917,0.0004156368,0.0005832176,0.0006976999,0.0004399644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005504237,"about_ca_system_score_gemma":0.0001595301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008043856,"about_ca_topic_score_gemma":0.001043147,"domain_scores_codex":[0.9995479,0.00005048023,0.00002564325,0.0001587387,0.000174738,0.00004252857],"domain_scores_gemma":[0.9992261,0.0002875045,0.0001832394,0.0001275302,0.0001109593,0.000064673],"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":[0.0004684139,0.0000500169,0.05763296,0.000222169,0.0001293609,0.0001634358,0.0001824334,0.006505219,0.9093068,0.001135561,0.0004982245,0.0237055],"study_design_scores_gemma":[0.00002984555,0.0008821781,0.3404482,0.0000591482,0.0002309947,0.001911516,0.0003144687,0.04880251,0.5807249,0.003921089,0.02259927,0.00007584979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9641709,0.001455382,0.02680292,0.0001475522,0.00003018066,0.00004072927,0.005131716,0.000421304,0.001799207],"genre_scores_gemma":[0.9858087,0.0004380153,0.007199935,0.00008630101,0.000008141503,0.00004542742,0.005451051,0.0001179512,0.000844516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001796192,"threshold_uncertainty_score":0.006008863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009483514546632831,"score_gpt":0.2179066090857932,"score_spread":0.2084230945391604,"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."}}