{"id":"W2140163842","doi":"10.1093/bioinformatics/btu178","title":"rTANDEM, an R/Bioconductor package for MS/MS protein identification","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Bioconductor; R package; Computer science; Pipeline (software); Identification (biology); JavaScript; Interface (matter); Software; Data mining; Operating system; Programming language; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001964512,0.0001371311,0.0001356627,0.00003328458,0.0001662507,0.00008293966,0.0002809458,0.0001303389,0.00008576999],"category_scores_gemma":[0.00008823034,0.0001296111,0.00006020629,0.00007034083,0.0000459282,0.0003487163,0.00003407489,0.00009807648,0.00004692326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004428803,"about_ca_system_score_gemma":0.00002325452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004588594,"about_ca_topic_score_gemma":0.000003214419,"domain_scores_codex":[0.9991021,0.000004580157,0.0004304465,0.0001442691,0.0001151269,0.0002034635],"domain_scores_gemma":[0.9988967,0.0000277268,0.0002700674,0.0006194557,0.00009770181,0.00008841322],"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":[0.00001913849,0.0001024255,0.00007015794,0.0005198391,0.00001445702,5.768619e-8,0.0003420857,0.000008639863,0.8952639,0.07545745,0.001140831,0.02706105],"study_design_scores_gemma":[0.0003170666,0.00004248113,0.00002166854,0.00003335489,0.00001343723,0.000002237118,0.00009581744,0.02086872,0.8857942,0.01622706,0.07633831,0.0002456378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1666907,0.000008333807,0.8242201,0.0001280675,0.00002963866,0.0008438958,0.0001527197,0.0003820273,0.007544511],"genre_scores_gemma":[0.5291407,0.000008643942,0.4669257,0.0001144023,0.0002303435,0.001247093,0.0005592831,0.00004124313,0.001732566],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.36245,"threshold_uncertainty_score":0.5285388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02169888587174565,"score_gpt":0.2854976449606821,"score_spread":0.2637987590889365,"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."}}