{"id":"W2729955529","doi":"10.1016/j.ymeth.2017.07.005","title":"RNA fluorescence in situ hybridization for high-content screening","year":2017,"lang":"en","type":"article","venue":"Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"RNA; Oligonucleotide; Fluorescence in situ hybridization; In situ hybridization; Fluorescence microscope; Computational biology; Messenger RNA; Biology; Fish <Actinopterygii>; Fluorescence; Molecular biology; In situ; Chemistry; Cell biology; Biochemistry; DNA; Gene; Physics","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":[0.0005145234,0.00008751669,0.0001153713,0.00002342162,0.000138845,0.00004855548,0.0002179857,0.00008146207,0.000002365461],"category_scores_gemma":[0.0003862771,0.00008513426,0.00005391625,0.00001731257,0.00004379503,0.000005439434,0.00004472212,0.00004973449,5.858431e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007301559,"about_ca_system_score_gemma":0.00001837248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001321256,"about_ca_topic_score_gemma":0.00008803549,"domain_scores_codex":[0.9993501,0.00007354295,0.0001420303,0.0002288861,0.0000461441,0.0001593152],"domain_scores_gemma":[0.9994925,0.00002032835,0.00007532124,0.0003235534,0.00005306042,0.00003525463],"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":[0.00007199457,0.00001964739,0.002903579,0.00001044623,0.000008308744,7.315495e-7,0.00001933674,0.00002252611,0.8749954,0.0001152538,0.00005016532,0.1217826],"study_design_scores_gemma":[0.0006867215,0.00008634817,0.02022553,0.00001758954,0.000008701421,0.00000101669,0.00001212817,0.0005791784,0.9750751,0.0001796345,0.003010645,0.0001174099],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4015988,0.000104914,0.5975175,0.0001192069,0.0002405326,0.000128951,0.000004546915,0.000005164577,0.0002804603],"genre_scores_gemma":[0.6478214,0.00003330693,0.3516665,0.00007044892,0.000133279,0.0000159343,0.00002975485,0.00001109838,0.0002183083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2462227,"threshold_uncertainty_score":0.3471674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08970965131354716,"score_gpt":0.3577968560183875,"score_spread":0.2680872047048403,"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."}}