{"id":"W4318071669","doi":"10.1038/s41587-022-01648-w","title":"Spatial transcriptomics for profiling the tropism of viral vectors in tissues","year":2023,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; University of California, Davis; National Alliance for Research on Schizophrenia and Depression; University of California, San Diego; California National Primate Research Center; National Institutes of Health; California Institute of Technology","keywords":"Profiling (computer programming); Transcriptome; Tropism; Computational biology; Biology; Virology; Gene expression profiling; Tissue tropism; Computer science; Virus; Genetics; Gene; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002809358,0.0001117091,0.0001519042,0.0001748602,0.00004343304,0.000004829925,0.0003993112,0.0009959087,0.000003324469],"category_scores_gemma":[0.0001569883,0.00008585813,0.0000801513,0.0003002706,0.0001862059,0.00000148236,0.00006621532,0.0004353105,0.000002772985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001528489,"about_ca_system_score_gemma":0.00006888967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002117167,"about_ca_topic_score_gemma":0.000463601,"domain_scores_codex":[0.9991086,0.00004684952,0.0001707964,0.0002770082,0.0001006104,0.0002961692],"domain_scores_gemma":[0.9995133,0.00002612097,0.00004498722,0.0003530958,0.0000455512,0.00001690748],"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":[0.0008201703,0.00002189043,0.0002886639,0.0000205437,0.0000285131,0.000001691906,0.00002870652,0.00002379078,0.984531,0.0007680028,0.0002934997,0.01317352],"study_design_scores_gemma":[0.0006224209,0.001297086,0.0003852431,0.000005804873,0.000004952834,0.000002080613,0.00006118655,0.0002803887,0.9825795,0.0006003242,0.01407265,0.00008839982],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940187,0.001717914,0.000343301,0.003103173,0.0002128608,0.0004955746,0.00004762532,0.0000426373,0.00001820617],"genre_scores_gemma":[0.9986899,0.0004113159,0.0003947581,0.0001040877,0.0001280906,0.00006681612,0.00009343386,0.00002425546,0.00008729124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01377915,"threshold_uncertainty_score":0.7681361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118544347386063,"score_gpt":0.3052183482867787,"score_spread":0.2940329048129181,"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."}}