{"id":"W2559724803","doi":"10.1021/acschembio.6b00855","title":"High-throughput Identification of DNA-Encoded IgG Ligands that Distinguish Active and Latent<i>Mycobacterium tuberculosis</i>Infections","year":2016,"lang":"en","type":"article","venue":"ACS Chemical Biology","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIH Office of the Director; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; National Institutes of Health; Bill and Melinda Gates Foundation","keywords":"Epitope; Mycobacterium tuberculosis; Antibody; Antibody Repertoire; Computational biology; Antigen; Biology; Recombinant DNA; DNA sequencing; Epitope mapping; DNA; Tuberculosis; Genetics; Gene; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003714958,0.0004184389,0.0005445342,0.0005313929,0.0002100881,0.000664808,0.0003235873,0.0003345965,0.001178705],"category_scores_gemma":[0.0004287908,0.0002451499,0.0002755236,0.0003729485,0.0002129964,0.0002559569,0.0004407178,0.0004392994,0.0005357573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004307734,"about_ca_system_score_gemma":0.0003484775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006408839,"about_ca_topic_score_gemma":0.001554599,"domain_scores_codex":[0.9997048,0.00004582943,0.00001700781,0.0000561288,0.000120517,0.00005567432],"domain_scores_gemma":[0.9998524,0.00004407318,0.00002712777,0.00001890278,0.00002703725,0.00003032579],"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":[0.0002271075,0.0001289826,0.001855269,0.00006554684,0.0000164817,0.00003509008,0.00001912483,0.0007944549,0.9856334,0.00008358389,0.0001791646,0.01096169],"study_design_scores_gemma":[0.00007470129,0.001018631,0.01726195,0.00001273181,0.00006960668,0.0003594061,0.00006142312,0.009156125,0.969042,0.0001617072,0.002761024,0.0000205156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834485,0.0009545767,0.01129522,0.0001960403,0.00001727098,0.0001647272,0.001648008,0.0003606163,0.001915004],"genre_scores_gemma":[0.9659931,0.0008029895,0.02546886,0.000217377,0.00001860373,0.0001309194,0.004125321,0.00003907337,0.003203643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001178705,"threshold_uncertainty_score":0.003943205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224266621961363,"score_gpt":0.3032868932948234,"score_spread":0.2810442270752098,"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."}}