{"id":"W2904522373","doi":"10.1182/bloodadvances.2017014639","title":"CapTCR-seq: hybrid capture for T-cell receptor repertoire profiling","year":2018,"lang":"en","type":"article","venue":"Blood Advances","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Princess Margaret Cancer Centre; Calgary Laboratory Services; University of Calgary; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Ontario Ministry of Research and Innovation; Canada Research Chairs; Princess Margaret Cancer Foundation","keywords":"T-cell receptor; Biology; Sanger sequencing; Polymerase chain reaction; Molecular biology; Locus (genetics); Gene; DNA sequencing; genomic DNA; Population; Gene rearrangement; Genetics; T cell","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":[0.001011626,0.000897796,0.0007485789,0.0009129916,0.0005620256,0.0009854115,0.001177816,0.0008308855,0.004575009],"category_scores_gemma":[0.0007917925,0.0004129971,0.0004980961,0.0006693077,0.0003608953,0.0003620433,0.0006081864,0.0009481551,0.002697922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004215609,"about_ca_system_score_gemma":0.0003657481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001142784,"about_ca_topic_score_gemma":0.003563997,"domain_scores_codex":[0.9990647,0.0001424743,0.00003354569,0.0003373976,0.0003263966,0.00009533823],"domain_scores_gemma":[0.9993908,0.0002017387,0.00006255375,0.00009755547,0.0001767802,0.00007048834],"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":[0.0001018985,0.00003433629,0.001099505,0.0001062619,0.00005276854,0.00004125671,0.00002963196,0.0006776326,0.9884126,0.0001535993,0.001405112,0.007885452],"study_design_scores_gemma":[0.0000355209,0.0002474914,0.01195446,0.00002114879,0.0001098585,0.0004487889,0.00006339741,0.0443183,0.9263421,0.0004888096,0.01590396,0.00006609196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2837795,0.00237262,0.6687744,0.0003164069,0.000242972,0.00098525,0.0221884,0.01123792,0.01010258],"genre_scores_gemma":[0.4662543,0.001200193,0.4885032,0.001578501,0.0001679788,0.001954807,0.02930554,0.001840846,0.009194613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004575009,"threshold_uncertainty_score":0.01530492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957654964476212,"score_gpt":0.3100120321077201,"score_spread":0.290435482462958,"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."}}