{"id":"W4405041947","doi":"10.1182/blood-2024-201725","title":"Blood-Based Proteomic Profiling Identifies Osmr As a Novel Biomarker","year":2024,"lang":"en","type":"article","venue":"Blood","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Genentech; Servier; Astellas Pharma; Daiichi-Sankyo; Ipsen; Syndax Pharmaceuticals; Incyte; Bristol-Myers Squibb; AstraZeneca; Foghorn Therapeutics; Astex Pharmaceuticals; Celgene; Ipsen Biopharmaceuticals; Regeneron Pharmaceuticals; Gilead Sciences; Chugai Pharmaceutical; Menarini Group; Agios Pharmaceuticals; GlaxoSmithKline; Amgen","keywords":"Biomarker; Profiling (computer programming); Computational biology; Biology; Medicine; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007466787,0.0003721668,0.0002787077,0.0008054824,0.0001376683,0.0006832146,0.0001656258,0.0003008948,0.000573015],"category_scores_gemma":[0.0009705987,0.0001056141,0.000254735,0.0005647224,0.0002099595,0.000263736,0.0004100732,0.0003827271,0.0003519122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000255193,"about_ca_system_score_gemma":0.0002473826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001973562,"about_ca_topic_score_gemma":0.0003089916,"domain_scores_codex":[0.9996681,0.00008661014,0.00004043147,0.00007394824,0.00009916302,0.0000317907],"domain_scores_gemma":[0.9994695,0.000112656,0.0002314634,0.00003698865,0.0000928236,0.00005657432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001598481,0.000171927,0.3860607,0.0002792448,0.0002035416,0.0005210074,0.0001267513,0.001215047,0.5665375,0.0003652878,0.000681287,0.04223928],"study_design_scores_gemma":[0.00007803817,0.001589368,0.6393207,0.00007052359,0.0003578988,0.003030828,0.0002031859,0.02318106,0.3252425,0.000880619,0.006009337,0.00003594968],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990741,0.00176409,0.006235788,0.0001594958,0.000018801,0.00002887758,0.0004680488,0.00005491867,0.00052895],"genre_scores_gemma":[0.9891924,0.0006533707,0.008824986,0.00009302372,0.00002888448,0.00002706861,0.0007823234,0.00000806073,0.0003898719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008054824,"threshold_uncertainty_score":0.003948867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173241895008815,"score_gpt":0.2868026383596222,"score_spread":0.2694784488587407,"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."}}