{"id":"W7115040063","doi":"","title":"Automating variant peptide target selection to streamline proteogenomic assay development with Mass Spectrometry","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Jewish General Hospital; Fondation De Famille Alvin Segal; Genome Canada; Warren Y. Soper Charitable Trust; McGill University","keywords":"Proteogenomics; Selection (genetic algorithm); Peptide; Peptide mapping; Mass spectrometry; Proteomics","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":[0.007179546,0.002172766,0.001807755,0.002060422,0.0008122203,0.003229118,0.001348171,0.001090245,0.006257191],"category_scores_gemma":[0.01074422,0.001500554,0.001909741,0.001123319,0.0006582023,0.001504898,0.001945539,0.002905148,0.007422904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008483689,"about_ca_system_score_gemma":0.001583932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009710653,"about_ca_topic_score_gemma":0.001877273,"domain_scores_codex":[0.9963652,0.0005480996,0.000391482,0.001062254,0.001336145,0.0002968617],"domain_scores_gemma":[0.9955178,0.002394192,0.0004624334,0.0005434987,0.000867441,0.0002146786],"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":[0.001343187,0.0002660342,0.008270388,0.001057172,0.0003692497,0.0007326431,0.0006423618,0.007496206,0.857348,0.002970836,0.01482806,0.1046759],"study_design_scores_gemma":[0.0001235734,0.0003754043,0.006252136,0.00008672887,0.0001537214,0.001017653,0.0001143344,0.08030293,0.8710596,0.002622961,0.03765826,0.0002327233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07757314,0.001153063,0.8556116,0.0005269608,0.0002233937,0.001183626,0.007637113,0.05286627,0.00322482],"genre_scores_gemma":[0.1073928,0.0007472902,0.868621,0.0005853556,0.00006716542,0.001426996,0.01134756,0.007360207,0.002451684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007179546,"threshold_uncertainty_score":0.03796953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008665982430103542,"score_gpt":0.2412989745995691,"score_spread":0.2326329921694656,"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."}}