{"id":"W2803314046","doi":"10.1101/328807","title":"CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer","year":2018,"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 Waterloo","funders":"National Institutes of Health; Chan Zuckerberg Initiative; Johns Hopkins University; Silicon Valley Community Foundation","keywords":"Benchmarking; Benchmark (surveying); R package; Ground truth; Set (abstract data type); Data set; Statistical model; Expression (computer science)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004097595,0.0004275856,0.0004891873,0.00007424218,0.0001100032,0.0001602212,0.0004260519,0.0004550974,0.00001594569],"category_scores_gemma":[0.000119919,0.000450214,0.00003272571,0.00007140095,0.0001266313,0.00002155293,0.0006758267,0.0002414805,0.000002796365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005635806,"about_ca_system_score_gemma":0.0002938478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009458799,"about_ca_topic_score_gemma":0.00002458011,"domain_scores_codex":[0.9975762,0.00007763752,0.0004365947,0.001286652,0.0001635464,0.0004593814],"domain_scores_gemma":[0.9981619,0.0000393168,0.0001282767,0.001121685,0.0001951572,0.000353704],"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":[0.0001593477,0.0002432217,0.000827765,0.0003611899,0.00003089462,0.000006030886,0.00002414009,0.0006062097,0.9976639,0.00001001101,0.00006187729,0.000005411397],"study_design_scores_gemma":[0.001164491,0.0003505864,0.0007013747,0.0003729,0.0001029491,4.792067e-8,0.000005015924,0.1016439,0.894555,0.000009581033,0.0003400747,0.0007540719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623587,0.0008496873,0.03363709,0.00003794334,0.0002728932,0.0007919602,0.002012067,0.00003851568,0.000001191779],"genre_scores_gemma":[0.965914,0.0001765245,0.03314466,0.0001298136,0.0003710446,0.0001127534,0.0000233922,0.0001237635,0.00000404622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1031088,"threshold_uncertainty_score":0.999795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988640878750287,"score_gpt":0.2802232282075331,"score_spread":0.2403368194200302,"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."}}