{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001916173,0.0001135868,0.0002391127,0.0003774206,0.0002871206,0.00009963665,0.0006715616,0.0001305229,0.00007879668],"category_scores_gemma":[0.0005988942,0.0001006622,0.000115918,0.0006342328,0.00006900584,0.0001699246,0.0001154432,0.0007293912,0.000005909899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014897,"about_ca_system_score_gemma":0.0004410645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008524132,"about_ca_topic_score_gemma":0.000001892126,"domain_scores_codex":[0.998369,0.00004621051,0.0004830321,0.000236053,0.0004307081,0.0004349747],"domain_scores_gemma":[0.9979288,0.0001991118,0.000156867,0.0004107052,0.001106414,0.0001981132],"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":[0.0003142265,0.0001199276,0.0004897473,0.00019627,0.00003359723,0.000005842242,0.00005269058,0.00008521401,0.9775547,0.002978161,0.0009357056,0.01723393],"study_design_scores_gemma":[0.0004621159,0.0001867799,0.00005656929,0.0003441687,0.000006767199,0.00000754858,0.0001239427,0.0006205569,0.9293386,0.04307352,0.02567055,0.0001089004],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5493065,0.0002615452,0.4401763,0.00358589,0.00005062398,0.002272063,0.00004229533,0.0000764322,0.004228356],"genre_scores_gemma":[0.2479687,0.0001026317,0.7481528,0.00006939394,0.0004664753,0.0009118852,0.000008706473,0.00003868102,0.002280809],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3079764,"threshold_uncertainty_score":0.4104884,"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."}}