{"id":"W4407388488","doi":"10.1093/bioinformatics/btaf060","title":"ImmunoTar—integrative prioritization of cell surface targets for cancer immunotherapy","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Immunotherapy and Biomarkers","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; Spinal Cord Injury BC","funders":"National Cancer Institute; Cancer Research UK; National Institutes of Health; Michael Smith Health Research BC; Mark Foundation For Cancer Research","keywords":"Identification (biology); Chimeric antigen receptor; Prioritization; Immunotherapy; Computational biology; Cancer immunotherapy; Cancer; Computer science; Proteomics; Bioinformatics; Biology; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001809552,0.0001530932,0.0003177937,0.00009850412,0.00005924995,0.00001589644,0.000100778,0.0001030581,0.00005207249],"category_scores_gemma":[0.00002530142,0.0001190109,0.0001736506,0.00030032,0.00008310223,0.0001184503,0.00001446089,0.00007828797,0.000001835344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001137938,"about_ca_system_score_gemma":0.0003778651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001099193,"about_ca_topic_score_gemma":0.00001428027,"domain_scores_codex":[0.9990473,0.00001123827,0.0005318347,0.00008825021,0.0001418145,0.0001795292],"domain_scores_gemma":[0.9991382,0.00007787947,0.0002231831,0.000237239,0.0002986362,0.00002489676],"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":[0.01072667,0.0007079027,0.007956355,0.004574968,0.001622956,0.000001081249,0.01198619,0.0001523402,0.8001474,0.001817367,0.03168486,0.128622],"study_design_scores_gemma":[0.01012774,0.0006928477,0.008792914,0.0009844825,0.0001916707,0.00000314293,0.002827423,0.01351669,0.8064471,0.0002338919,0.1558439,0.0003380899],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5891412,0.04606321,0.3196099,0.003567992,0.003420579,0.00788907,0.0007083463,0.0005027901,0.02909695],"genre_scores_gemma":[0.7964291,0.01505313,0.1699719,0.003418518,0.0001075352,0.0001999227,0.000336832,0.00009871749,0.01438436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2072879,"threshold_uncertainty_score":0.4853125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063801571251491,"score_gpt":0.2905621301833129,"score_spread":0.2799241144707981,"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."}}