{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002723833,0.0002056778,0.0003363373,0.00009165033,0.0001872613,0.00002896397,0.0001761175,0.00008069843,0.001305891],"category_scores_gemma":[0.0002310072,0.0001665982,0.0001459155,0.0001819374,0.0002113005,0.0001474397,0.00004482109,0.0002233508,0.00009714461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004834454,"about_ca_system_score_gemma":0.0001657354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002087871,"about_ca_topic_score_gemma":0.00002204029,"domain_scores_codex":[0.9983202,0.00003389563,0.0002637318,0.0005012078,0.0003715152,0.0005095075],"domain_scores_gemma":[0.9987728,0.0001328532,0.00008706943,0.0004701013,0.0003470557,0.0001901085],"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":[0.0008758876,0.0003900531,0.004064417,0.0003931677,0.00008950083,0.00003414854,0.0003087285,0.000001506141,0.957289,0.00008412558,0.005115105,0.0313544],"study_design_scores_gemma":[0.00214351,0.0007032222,0.00002857569,0.00007312107,0.00006053362,0.00005737776,0.0002700404,0.00005965692,0.6498666,0.0001718094,0.3464118,0.0001537957],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404412,0.01485255,0.0005802906,0.002036305,0.001070233,0.002768638,0.0001100892,0.000316245,0.03782449],"genre_scores_gemma":[0.8129902,0.001316196,0.03900627,0.001163049,0.004812949,0.0004323442,0.0001938647,0.0001774335,0.1399076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3412966,"threshold_uncertainty_score":0.999607,"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."}}