{"id":"W2513487313","doi":"10.1038/srep31730","title":"Complete De Novo Assembly of Monoclonal Antibody Sequences","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":172,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Key Research and Development Program of China; Scheme for Promotion of Academic and Research Collaboration; Hospital for Sick Children","keywords":"Sequence assembly; Computational biology; Monoclonal antibody; De Bruijn sequence; Genome; Proteomics; Immunoglobulin light chain; De Bruijn graph; Computer science; Biology; Antibody; Graph; Gene; Genetics; Mathematics; Combinatorics; Transcriptome; Theoretical 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":[0.0003730809,0.00008083125,0.0001184148,0.00003964045,0.0001272738,0.00003473297,0.0001662106,0.00005079692,0.0002804805],"category_scores_gemma":[0.00005949113,0.00005816779,0.00006344268,0.000137464,0.0002851479,0.00009733978,0.00006124393,0.00004798561,0.000008060647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004408724,"about_ca_system_score_gemma":0.0001132602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985599,"about_ca_topic_score_gemma":0.000003141224,"domain_scores_codex":[0.9988659,0.000005262051,0.0003352884,0.0003830735,0.0002103628,0.0002001111],"domain_scores_gemma":[0.9988577,0.00003358827,0.0002958652,0.0006282094,0.0001178359,0.0000667377],"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":[0.000001886284,0.00002259175,0.005795422,0.00001308009,0.00000436923,0.00002957529,0.00001621014,0.000004162455,0.9911637,0.001305492,0.0004043416,0.00123913],"study_design_scores_gemma":[0.00003976508,0.000004020684,0.0001045469,0.00005405181,0.000004543222,0.0001766109,0.000009327579,0.00002938298,0.9072077,0.06222203,0.03006251,0.00008546869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457406,0.00003396066,0.04611571,0.0002041533,0.000177437,0.00008506577,0.000031591,0.00009746833,0.007514002],"genre_scores_gemma":[0.9631987,0.000008217805,0.03415383,0.000007509048,0.00003324025,0.00003227416,0.0000288651,0.000009205087,0.002528184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.083956,"threshold_uncertainty_score":0.3071065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02414833861714541,"score_gpt":0.3142664562418849,"score_spread":0.2901181176247395,"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."}}