{"id":"W4412163869","doi":"10.1158/1557-3265.aimachine-b037","title":"Abstract B037: AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Proteomics; Computational biology; Medicine; Data science; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003165515,0.0006864697,0.0006633161,0.001083254,0.0003321388,0.001901982,0.001718038,0.0008690059,0.006396228],"category_scores_gemma":[0.005908688,0.0004890698,0.001313847,0.0008240168,0.001065901,0.001715756,0.002861903,0.001464067,0.002275273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125494,"about_ca_system_score_gemma":0.002241199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002483536,"about_ca_topic_score_gemma":0.004229876,"domain_scores_codex":[0.9991854,0.0003571495,0.0000399755,0.0001376364,0.0002228994,0.00005696575],"domain_scores_gemma":[0.99775,0.0009841268,0.0001835708,0.0005512124,0.0003046361,0.000226534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001033539,0.0002026123,0.007285427,0.0006540333,0.0003538492,0.000555562,0.0003766847,0.3794835,0.03389088,0.1497592,0.05191536,0.3744893],"study_design_scores_gemma":[0.00005637962,0.0001199599,0.0009315128,0.00004160147,0.00004202387,0.0002143854,0.00004704754,0.8446079,0.009321149,0.1252261,0.01935782,0.00003405596],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02006808,0.0006929112,0.9629644,0.002344897,0.000247755,0.0001518331,0.001928137,0.004905713,0.006696262],"genre_scores_gemma":[0.4702757,0.001045783,0.5085891,0.001087371,0.0002614714,0.0004750582,0.004828666,0.0007602765,0.0126767],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.006396228,"threshold_uncertainty_score":0.02139753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5421546771818586,"score_gpt":0.6767384420996166,"score_spread":0.134583764917758,"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."}}