{"id":"W4406409991","doi":"10.1021/acs.jproteome.4c00837","title":"IP-to-MS: An Unbiased Workflow for Antigen Profiling","year":2025,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); IONICS Mass Spectrometry (Canada)","funders":"National Institute of General Medical Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Immunoprecipitation; Computational biology; Antibody; Profiling (computer programming); Workflow; Proteomics; Antigen; Chemistry; Computer science; Biology; Biochemistry; Immunology; Gene; Database","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00236904,0.001606048,0.001133099,0.002202359,0.0009862722,0.00157412,0.001950567,0.00129563,0.0025439],"category_scores_gemma":[0.002163166,0.0007383093,0.0006944653,0.001133827,0.0004729164,0.001124436,0.002013617,0.002088861,0.005010857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004466269,"about_ca_system_score_gemma":0.001385457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005714681,"about_ca_topic_score_gemma":0.001243152,"domain_scores_codex":[0.9976707,0.0003805394,0.0002220232,0.0006025761,0.0009484669,0.0001756797],"domain_scores_gemma":[0.9988285,0.0002627407,0.0001507074,0.0003089819,0.0003489939,0.0001000491],"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":[0.0002430773,0.000122237,0.001559442,0.0003588057,0.0001053455,0.0002431733,0.000078713,0.0005523514,0.9357535,0.001097036,0.007020727,0.05286569],"study_design_scores_gemma":[0.00006125792,0.0002335499,0.00541851,0.00005772937,0.0000911208,0.001060739,0.00005121773,0.02147722,0.9189374,0.002799669,0.04966903,0.0001425821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0250763,0.002044805,0.9493292,0.0005411941,0.000354609,0.001060745,0.006299085,0.01231934,0.002974688],"genre_scores_gemma":[0.06716673,0.002084497,0.9144835,0.0009538007,0.0001931664,0.001762119,0.007895166,0.001346733,0.004114367],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0025439,"threshold_uncertainty_score":0.01252884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0913327058732064,"score_gpt":0.4655664421780376,"score_spread":0.3742337363048312,"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."}}