{"id":"W3217246491","doi":"10.1093/nargab/lqab105","title":"RNA-Scoop: interactive visualization of transcripts in single-cell transcriptomes","year":2021,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Human Genome Research Institute; National Institutes of Health; Genome British Columbia; Canada's Michael Smith Genome Sciences Centre; Genome Canada","keywords":"Transcriptome; RNA; Computational biology; Visualization; Biology; Cell; Single-cell analysis; Computer science; Genetics; Gene expression; Gene; Data mining","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":[0.0001324293,0.0001808592,0.0002506641,0.00009438066,0.00004352483,0.00004052901,0.000112355,0.0001830198,0.00001041759],"category_scores_gemma":[0.00002435183,0.0001838806,0.00009658774,0.0001648239,0.00007660336,0.00001927298,0.00003197186,0.00009848466,0.000001486558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002587031,"about_ca_system_score_gemma":0.0001116441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001726587,"about_ca_topic_score_gemma":0.00009225534,"domain_scores_codex":[0.9989251,0.00003505922,0.0005224615,0.0001918918,0.0001134041,0.0002120392],"domain_scores_gemma":[0.999469,0.0000149776,0.0001290032,0.0001877388,0.0001293652,0.00006988895],"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":[0.0001205573,0.0002558457,0.001369182,0.000215655,0.00003251334,0.000003199505,0.00213214,0.00007845152,0.9887475,0.00009460849,0.00006693273,0.00688337],"study_design_scores_gemma":[0.001719977,0.000378849,0.000721767,0.00005865133,0.00003480254,0.00002123415,0.001429649,0.003641653,0.9833239,0.00005518279,0.008320272,0.0002940455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844031,0.001283649,0.01095187,0.00005306759,0.0002170912,0.0001618838,0.00005915186,0.000008040211,0.002862176],"genre_scores_gemma":[0.993282,0.001513225,0.004502859,0.0002699897,0.00004745847,0.000003850119,0.0002149802,0.00002255469,0.0001430412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008878967,"threshold_uncertainty_score":0.7498433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306987496882045,"score_gpt":0.228204744840605,"score_spread":0.2151348698717845,"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."}}