{"id":"W269536890","doi":"10.1016/j.jim.2015.04.021","title":"X-FISH: Analysis of cellular RNA expression patterns using flow cytometry","year":2015,"lang":"en","type":"article","venue":"Journal of Immunological Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Flow cytometry; Computational biology; RNA; Biology; Fish <Actinopterygii>; Population; In situ hybridization; Messenger RNA; Cell culture; Gene expression; Fluorescence in situ hybridization; Molecular biology; Cell biology; Genetics; Gene","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":[0.00142481,0.0001493331,0.0005345817,0.0002551951,0.0000345674,0.000017531,0.0003242837,0.000270726,0.00003858045],"category_scores_gemma":[0.0005942408,0.0001058688,0.0004657056,0.0004051568,0.00008071288,0.00001047935,0.00009266558,0.0002401854,2.752361e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002950604,"about_ca_system_score_gemma":0.00005601858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001190886,"about_ca_topic_score_gemma":7.314932e-7,"domain_scores_codex":[0.9982049,0.0006050026,0.0006183248,0.0001688159,0.000218841,0.0001840669],"domain_scores_gemma":[0.9987403,0.00007143711,0.0004804954,0.0002562927,0.0003389257,0.0001125219],"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":[0.0003260035,0.0001634895,0.004876693,0.000007938924,0.0006060818,0.00001209763,0.00006023174,0.001294207,0.9822277,0.000002171197,0.00001447298,0.01040891],"study_design_scores_gemma":[0.0006644535,0.0007095538,0.003637716,0.00003105368,0.0004608686,0.00001741975,0.0001670401,0.001413737,0.9919881,0.00004378494,0.0007321736,0.0001341424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5897184,0.001317108,0.4086157,0.00001554222,0.0002450683,0.00002430492,0.000007035304,0.00000217222,0.00005464565],"genre_scores_gemma":[0.7449361,0.0002393667,0.2546139,0.00005716043,0.0001192785,4.311268e-7,0.000009206616,0.000009502049,0.000015061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1552177,"threshold_uncertainty_score":0.4317206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0890733527811519,"score_gpt":0.3654058180579204,"score_spread":0.2763324652767685,"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."}}