{"id":"W4293148275","doi":"10.1055/s-0042-1746579","title":"Identification of a predictive marker signature for diagnosing HNSCC based on platelet RNAseq","year":2022,"lang":"en","type":"article","venue":"Laryngo-Rhino-Otologie","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"","keywords":"Identification (biology); Computer science; Signature (topology); Predictive marker; Computational biology; Artificial intelligence; Medicine; Biology; Cancer; Internal medicine; Mathematics","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":[0.0004953653,0.0002253453,0.0002266891,0.00009133283,0.000217304,0.00002205083,0.0004533817,0.0002014861,0.0001109562],"category_scores_gemma":[0.0006508974,0.0002366503,0.00022951,0.0001588988,0.0001155977,0.000008753365,0.0002137003,0.0002369955,0.000004327565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006974735,"about_ca_system_score_gemma":0.0001502444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006744935,"about_ca_topic_score_gemma":0.000005077424,"domain_scores_codex":[0.998184,0.0001800546,0.0003864357,0.0006186954,0.0002967467,0.0003341293],"domain_scores_gemma":[0.9986205,0.0001825332,0.0002776177,0.0007192295,0.0001090926,0.00009109645],"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":[0.001960514,0.0006593138,0.00569544,0.0001232082,0.0001088637,0.00004693384,0.00004853479,0.009623383,0.962802,0.0001614113,0.01676642,0.002004014],"study_design_scores_gemma":[0.001707402,0.001031002,0.005225648,0.00002660528,0.00008240958,0.000009818272,0.0001389887,0.00970849,0.9649434,0.0006293931,0.01613132,0.0003655278],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854679,0.001952496,0.008569474,0.0004709776,0.0004078979,0.001280842,0.001424056,0.0000558616,0.0003705729],"genre_scores_gemma":[0.9961033,0.0000366762,0.0006504055,0.0007063332,0.0001160188,0.0007121728,0.001326005,0.00004723257,0.000301781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01063555,"threshold_uncertainty_score":0.9650319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009634300823012176,"score_gpt":0.2568169146520003,"score_spread":0.2471826138289882,"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."}}