{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038888,0.0001412131,0.0001308547,0.00004510818,0.0001093344,0.00004278273,0.0002571893,0.0001308946,0.000002963443],"category_scores_gemma":[0.00004540518,0.0001289181,0.0001369293,0.00006192345,0.00005437753,0.00000754455,0.00006527674,0.00007142635,0.000002647457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001633024,"about_ca_system_score_gemma":0.00005192962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001797047,"about_ca_topic_score_gemma":0.008813813,"domain_scores_codex":[0.9990877,0.00002582168,0.0002672787,0.0002919299,0.0000569631,0.0002702886],"domain_scores_gemma":[0.9994055,0.00002920173,0.00009376556,0.0003803345,0.00005811024,0.00003308404],"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":[0.00006455398,0.00006688075,0.0005299191,0.00001879413,0.00002483895,4.305231e-7,0.0002276626,0.00003136525,0.9964865,0.0001122535,0.000126307,0.002310491],"study_design_scores_gemma":[0.0002165193,0.0001167095,0.000324019,0.00001281536,0.00003243119,0.000002915489,0.0003942827,0.001281219,0.9921955,0.0004411904,0.004792969,0.0001893657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8796518,0.0003757586,0.1185955,0.000009050021,0.00006949752,0.0003448845,0.000007938392,0.00002196591,0.0009236066],"genre_scores_gemma":[0.9670471,0.0002625105,0.03200253,0.00005184384,0.00009016967,0.00005094463,0.00004344412,0.00003814654,0.0004133271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08739528,"threshold_uncertainty_score":0.5257129,"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."}}