{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631661,0.001228338,0.001328733,0.001398357,0.0005593221,0.001665007,0.001463286,0.000659664,0.00527923],"category_scores_gemma":[0.003350576,0.0003889385,0.001544173,0.001236132,0.0004048277,0.001112772,0.001020278,0.0009438872,0.001445659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008881041,"about_ca_system_score_gemma":0.00226814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003063201,"about_ca_topic_score_gemma":0.005407385,"domain_scores_codex":[0.999468,0.0001583604,0.00003587613,0.0001571847,0.0001308344,0.00004980955],"domain_scores_gemma":[0.99888,0.0007053846,0.00008879985,0.0000909407,0.0001479356,0.00008698246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002756957,0.001002493,0.04243423,0.002449452,0.001166015,0.0007186333,0.000375639,0.5560206,0.03697712,0.01946167,0.07580838,0.2608288],"study_design_scores_gemma":[0.0002160744,0.0002259662,0.001644639,0.00003970923,0.0001352455,0.0001253902,0.00006266436,0.9690553,0.009397278,0.009557073,0.009510884,0.00002970209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2936594,0.002842359,0.5800743,0.002713971,0.0004160854,0.0008861239,0.02884313,0.07496133,0.01560333],"genre_scores_gemma":[0.4854258,0.001219104,0.4617707,0.00119164,0.0001490605,0.001042476,0.04389693,0.002822324,0.00248186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00527923,"threshold_uncertainty_score":0.01766086,"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."}}