{"id":"W1988766191","doi":"10.1186/1471-2105-10-244","title":"ETISEQ – an algorithm for automated elution time ion sequencing of concurrently fragmented peptides for mass spectrometry-based proteomics","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Sydney; Australian Government; Ontario Ministry of Natural Resources and Forestry","keywords":"Fragmentation (computing); Shotgun proteomics; Proteomics; Peptide; Mass spectrometry; Data acquisition; Computer science; Quantitative proteomics; Tandem mass spectrometry; Chemistry; Computational biology; Chromatography; Biology; Biochemistry","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":[0.003035838,0.001627468,0.0007960284,0.001660336,0.0006933807,0.001261915,0.002100363,0.001130712,0.002878062],"category_scores_gemma":[0.003942406,0.0007180897,0.0008886055,0.001103579,0.0006701704,0.001213982,0.001341955,0.001752523,0.002244471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000701542,"about_ca_system_score_gemma":0.001288835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006421504,"about_ca_topic_score_gemma":0.0009381027,"domain_scores_codex":[0.998716,0.0003137178,0.0001805776,0.000292113,0.0004368939,0.00006062476],"domain_scores_gemma":[0.998503,0.0006760078,0.0002243672,0.0001364758,0.0003824813,0.00007774212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001692569,0.0004166905,0.004724792,0.0005979769,0.0003466346,0.0006171615,0.0003111415,0.04446804,0.1658243,0.008577473,0.01948019,0.7529431],"study_design_scores_gemma":[0.0003346175,0.0005322847,0.002150027,0.00006899003,0.00009070557,0.001209825,0.0000674554,0.8337325,0.1238324,0.009654541,0.02818613,0.0001404558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005276166,0.0001724465,0.9869415,0.00005280721,0.00005097786,0.0001581527,0.0001754126,0.006852701,0.000319809],"genre_scores_gemma":[0.01167306,0.0000790532,0.9866689,0.0000636031,0.00002111668,0.0002705338,0.0005447326,0.0002486629,0.0004303253],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003035838,"threshold_uncertainty_score":0.01605529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02039169974329908,"score_gpt":0.2938443584831975,"score_spread":0.2734526587398984,"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."}}