{"id":"W4399379462","doi":"10.1101/2024.06.04.597422","title":"IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency; Terry Fox Research Institute","funders":"National Cancer Institute; Cancer Research UK; National Institutes of Health; Michael Smith Health Research BC; Mark Foundation For Cancer Research","keywords":"Prioritization; Immunotherapy; Cancer immunotherapy; Cancer; Computational biology; Cancer research; Computer science; Medicine; Biology; Engineering; Internal medicine; Management science","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":[0.001973029,0.001318296,0.001469628,0.001829525,0.0005196465,0.002218758,0.001181548,0.000785246,0.004155788],"category_scores_gemma":[0.003215689,0.000526946,0.001793577,0.001405823,0.0003965824,0.001355717,0.00121121,0.001013421,0.001120205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008462793,"about_ca_system_score_gemma":0.00210283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428904,"about_ca_topic_score_gemma":0.005020888,"domain_scores_codex":[0.9994113,0.0001743784,0.00004461084,0.0001637134,0.0001503056,0.00005567698],"domain_scores_gemma":[0.9990457,0.0005574015,0.00007753301,0.0001163038,0.0001366174,0.00006641889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002690247,0.0008982175,0.03919989,0.002132461,0.00151629,0.0007556562,0.0004664655,0.5979056,0.07665491,0.0251588,0.06253619,0.1900853],"study_design_scores_gemma":[0.000174628,0.0001721895,0.001749307,0.00002792698,0.0001330469,0.0001163477,0.00006979358,0.9612549,0.016152,0.0111265,0.008985658,0.00003772212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3221015,0.002063251,0.5801625,0.001574763,0.0003246717,0.0005302894,0.03107004,0.04980527,0.01236762],"genre_scores_gemma":[0.5225043,0.0009664316,0.4285972,0.0007438247,0.0001118365,0.000905773,0.04043757,0.003119569,0.002613449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004155788,"threshold_uncertainty_score":0.01390249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713988653282295,"score_gpt":0.2873855314952395,"score_spread":0.2702456449624165,"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."}}