{"id":"W2068032316","doi":"10.1186/1471-2164-12-115","title":"The living microarray: a high-throughput platform for measuring transcription dynamics in single cells","year":2011,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; McGill University and Génome Québec Innovation Centre; McGill University","funders":"Ontario Ministry of Research and Innovation; Fondation de l'Hôpital Général de Montréal; Université de Genève; Canadian Institutes of Health Research; Genome Canada; McGill University Health Centre; University of Waterloo; Ontario Institute for Cancer Research; McGill University","keywords":"Biology; DNA microarray; Microarray; Computational biology; Dynamics (music); Throughput; Transcription (linguistics); Microarray analysis techniques; Proteomics; Genetics; Gene; Gene expression; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631607,0.001133782,0.00158632,0.001667956,0.0007827944,0.001469618,0.002107922,0.001708137,0.005772242],"category_scores_gemma":[0.001023557,0.0008039362,0.0009085056,0.001006779,0.0006986937,0.001190962,0.0009106593,0.002681541,0.004498168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007944354,"about_ca_system_score_gemma":0.0005826381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003753639,"about_ca_topic_score_gemma":0.0007889535,"domain_scores_codex":[0.9981562,0.0003633506,0.0001070159,0.0004112896,0.0008600348,0.0001021178],"domain_scores_gemma":[0.9991311,0.000312873,0.0001291059,0.0001633422,0.0001535329,0.0001099683],"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":[0.0001322027,0.0000715343,0.0002753946,0.0002583895,0.00004253849,0.00006217023,0.00003488085,0.0006150294,0.9772584,0.0009992131,0.004591327,0.01565899],"study_design_scores_gemma":[0.0001069964,0.0004755531,0.002605067,0.00005075832,0.00009164572,0.0005708716,0.00003543708,0.02083299,0.9316195,0.002000746,0.0414817,0.0001288749],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03164706,0.002994572,0.9340207,0.001218733,0.0006313814,0.0006010715,0.00724323,0.01760221,0.004041038],"genre_scores_gemma":[0.07770149,0.003866514,0.8961991,0.0009529063,0.0004632158,0.003331131,0.008213262,0.001236913,0.008035433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005772242,"threshold_uncertainty_score":0.01931006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02912879235265558,"score_gpt":0.2229304661935311,"score_spread":0.1938016738408755,"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."}}