{"id":"W4388213288","doi":"10.1101/2023.10.30.564469","title":"Pepid: a Highly Modifiable, Bioinformatics-Oriented Peptide Search Engine","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Institut de Valorisation des Données","keywords":"Identification (biology); Computer science; Software; Workflow; Process (computing); Mascot; Search engine; Database search engine; Software engineering; Data mining; Information retrieval; Programming language; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001812713,0.001255383,0.0009725458,0.001003048,0.0004871972,0.001648534,0.003357482,0.001063324,0.01217875],"category_scores_gemma":[0.004529928,0.0008381703,0.0009546098,0.001029372,0.0005920192,0.002830554,0.002890125,0.001800384,0.008387418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007651348,"about_ca_system_score_gemma":0.0016251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001341062,"about_ca_topic_score_gemma":0.001320072,"domain_scores_codex":[0.998946,0.0001254116,0.0001252942,0.0002799107,0.0004039316,0.0001194421],"domain_scores_gemma":[0.9986328,0.0005654729,0.00008419264,0.0002614147,0.0002714275,0.0001846097],"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":[0.007810255,0.0008514685,0.01074547,0.004583128,0.0005682073,0.001124738,0.0003342638,0.02795861,0.1338993,0.03823563,0.3339994,0.4398895],"study_design_scores_gemma":[0.001747264,0.000914074,0.004477378,0.0002131129,0.0002443654,0.001916932,0.0001163549,0.3835288,0.2474302,0.01700988,0.3419691,0.0004324864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0482449,0.003400628,0.5168653,0.001042454,0.0008120628,0.0009975743,0.02192468,0.3829311,0.02378138],"genre_scores_gemma":[0.2058382,0.001651917,0.6911742,0.001934478,0.0001840531,0.001402006,0.05916347,0.01794745,0.02070424],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01217875,"threshold_uncertainty_score":0.04074198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997391680327254,"score_gpt":0.2498756513459715,"score_spread":0.229901734542699,"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."}}