{"id":"W4407866599","doi":"10.1158/2326-6074.io2025-b108","title":"Abstract B108: nELISA high-throughput proteomics enables scalable biomarker discovery: identification of IL-1 pathway intermediates as novel CRC biomarkers","year":2025,"lang":"en","type":"article","venue":"Cancer Immunology Research","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Biomarker discovery; Proteomics; Computational biology; Biomarker; Identification (biology); Throughput; Biology; Computer science; Gene; Genetics","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":[0.0006009179,0.0001739436,0.0002151725,0.0002097689,0.0002205769,0.00005847581,0.000506317,0.0003119637,0.00001830086],"category_scores_gemma":[0.000290811,0.000163858,0.00008441696,0.0005029542,0.001019397,0.00002365701,0.0004274078,0.0002513629,0.000008752852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001115584,"about_ca_system_score_gemma":0.000482684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000674747,"about_ca_topic_score_gemma":0.00006687967,"domain_scores_codex":[0.9983212,0.00007555167,0.0004345606,0.0005694304,0.0001651039,0.000434193],"domain_scores_gemma":[0.9984792,0.00006857544,0.0001733911,0.0007708923,0.0004706542,0.00003730766],"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":[0.000291448,0.0001491606,0.0003812161,0.00006089174,0.0002059671,8.242109e-7,0.00001830728,0.00000913196,0.9868811,0.002203216,0.002175199,0.007623494],"study_design_scores_gemma":[0.000434549,0.00009813956,0.008530967,0.00009535299,0.0000117017,0.000002987619,0.0001583694,0.00002978983,0.9707099,0.002754236,0.01702678,0.0001472283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835322,0.002397633,0.01029287,0.00171401,0.0002468856,0.0008495967,0.0002997233,0.00003817932,0.0006288491],"genre_scores_gemma":[0.9923792,0.002746305,0.001945218,0.00007061722,0.00005986711,0.0004412877,0.0002733565,0.00002602921,0.002058053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01617125,"threshold_uncertainty_score":0.6681937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0316114799888591,"score_gpt":0.370225012542262,"score_spread":0.3386135325534029,"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."}}