{"id":"W2340776849","doi":"10.1371/journal.pone.0153901","title":"A Digital PCR-Based Method for Efficient and Highly Specific Screening of Genome Edited Cells","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Cancer Foundation; Alberta Innovates; Alberta Innovates - Health Solutions; Women and Children's Health Research Institute; Children's Health Research Institute","keywords":"Transcription activator-like effector nuclease; CRISPR; Genome editing; Amplicon; Computational biology; Biology; Digital polymerase chain reaction; Genetics; Cas9; Genome engineering; False positive paradox; Trans-activating crRNA; Gene; Polymerase chain reaction; 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":[0.00008354765,0.00008647223,0.0001250535,0.00003662534,0.00001874303,0.00001018835,0.00006374032,0.00005527879,0.000005375908],"category_scores_gemma":[0.00003369689,0.00006886048,0.00004438114,0.00003589816,0.00002824694,0.000001187508,0.00003534542,0.00001840588,0.000001055717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003841812,"about_ca_system_score_gemma":0.00000981926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.00805e-7,"about_ca_topic_score_gemma":2.73901e-7,"domain_scores_codex":[0.9994391,0.000007399823,0.0001279178,0.0001971403,0.00008618695,0.0001422497],"domain_scores_gemma":[0.9996462,0.0000413253,0.00003595915,0.0001633658,0.00006033973,0.00005275799],"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":[0.00007179118,0.0001233976,0.00007852232,0.00005018213,0.00006344101,2.820259e-7,0.000007236217,0.0004420814,0.9962166,0.000007086827,0.00004057573,0.002898824],"study_design_scores_gemma":[0.0007149003,0.0002649092,0.0003250924,0.00004551007,0.0000294929,4.342237e-7,0.000007053967,0.001713955,0.9939677,0.000004131981,0.002818664,0.00010814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4483132,0.0004260554,0.5508407,0.00006481059,0.00002055664,0.000149379,0.0001044595,0.000007384972,0.00007342207],"genre_scores_gemma":[0.9283513,0.00003250489,0.07110583,0.00001663751,0.0001913319,0.00001522451,0.00003204246,0.00001872714,0.0002363788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4800381,"threshold_uncertainty_score":0.2808049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987252638001832,"score_gpt":0.2527843634465602,"score_spread":0.2329118370665419,"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."}}