{"id":"W4388529482","doi":"10.21203/rs.3.rs-3520814/v1","title":"A complete mass spectrometry-based immunopeptidomics pipeline for neoantigen identification and validation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Pipeline (software); Mass spectrometry; Identification (biology); Computational biology; Computer science; Chromatography; Chemistry; Biology; Programming language","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":[0.002702139,0.002216618,0.001379803,0.0023894,0.001138052,0.003356296,0.001988408,0.001810563,0.01620435],"category_scores_gemma":[0.004040069,0.001131982,0.001401691,0.001057539,0.0005246826,0.002054572,0.00229776,0.002980971,0.01812655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007105183,"about_ca_system_score_gemma":0.001959728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009565746,"about_ca_topic_score_gemma":0.001468176,"domain_scores_codex":[0.9988093,0.0001175378,0.00008333704,0.0003151643,0.0005628745,0.0001117431],"domain_scores_gemma":[0.9985197,0.000400029,0.0001340667,0.0004222539,0.0004222962,0.000101695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001321733,0.0002777,0.004426427,0.001562295,0.0006145887,0.0009327063,0.0003121402,0.004997522,0.6346343,0.009848712,0.08533664,0.2557353],"study_design_scores_gemma":[0.0002830938,0.0002720472,0.006175952,0.0001880833,0.000208539,0.003028898,0.0001725825,0.06347147,0.6649474,0.034987,0.2260145,0.0002503807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02435058,0.002581495,0.8420562,0.001216256,0.0004861987,0.0005597647,0.01999629,0.09824065,0.01051269],"genre_scores_gemma":[0.08849942,0.001986575,0.8475745,0.001724467,0.0002478562,0.001035187,0.03669678,0.01140841,0.01082673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01620435,"threshold_uncertainty_score":0.05420899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0886318395134981,"score_gpt":0.36592560937644,"score_spread":0.277293769862942,"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."}}