{"id":"W1984709499","doi":"10.1016/j.jprot.2014.03.035","title":"2DE: The Phoenix of Proteomics","year":2014,"lang":"en","type":"review","venue":"Journal of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":136,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Proteome; Computational biology; Proteomics; Shotgun proteomics; Shotgun; Computer science; Data science; Proteogenomics; Posttranslational modification; Mass spectrometry; Focus (optics); Biology; Chemistry; Bioinformatics; Chromatography; Genomics; Biochemistry; Physics; Genome","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009560915,0.0004946171,0.002155728,0.0001495541,0.0001312285,0.00005447516,0.00172377,0.0005913433,0.00007449325],"category_scores_gemma":[0.0002795463,0.0003249464,0.001249073,0.0002789067,0.0002151496,0.0001058626,0.0002322557,0.001900123,0.00001181723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440092,"about_ca_system_score_gemma":0.0006372289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000421542,"about_ca_topic_score_gemma":9.531555e-7,"domain_scores_codex":[0.9966225,0.00008232484,0.002263553,0.0002846034,0.0004166019,0.0003303851],"domain_scores_gemma":[0.9923451,0.0002390874,0.005856552,0.001017335,0.0004120574,0.0001298578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009882892,0.0002927853,0.00000471384,0.03116237,0.0006418705,0.00001372882,0.0001196678,0.0000443242,0.01245998,0.005288539,0.00166688,0.9482063],"study_design_scores_gemma":[0.0002483134,0.00009273442,9.324139e-8,0.007789975,0.0006613542,0.0004358478,0.00001438407,0.00002073397,0.02086822,0.004672407,0.9648407,0.0003553049],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008618906,0.8741761,0.1222849,0.0001329946,0.0001159538,0.001609961,0.00009911192,0.00003909738,0.001455665],"genre_scores_gemma":[0.00001976533,0.7730333,0.2255596,0.00002147366,0.0006963996,0.0002787057,0.0000129022,0.0001004296,0.0002774484],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9631737,"threshold_uncertainty_score":0.9999202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03185584465843334,"score_gpt":0.3441537277337659,"score_spread":0.3122978830753326,"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."}}