{"id":"W4297445777","doi":"10.1101/2022.09.14.507872","title":"Seq2Neo: a comprehensive pipeline for cancer neoantigen immunogenicity prediction","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"ShanghaiTech University; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Immunogenicity; Computational biology; Cancer; Immunotherapy; Cancer immunotherapy; Pipeline (software); Biology; Computer science; Immune system; Immunology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001323933,0.002449211,0.001083973,0.001345984,0.000581464,0.001474348,0.001534617,0.0009632237,0.01424564],"category_scores_gemma":[0.003013317,0.0008774081,0.002341036,0.0007958672,0.0003433768,0.001077487,0.001273868,0.00148809,0.007796104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008615752,"about_ca_system_score_gemma":0.00184476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004765067,"about_ca_topic_score_gemma":0.007365219,"domain_scores_codex":[0.9994338,0.00007482164,0.00003782415,0.0002481864,0.0001360816,0.00006930392],"domain_scores_gemma":[0.9993325,0.0003232259,0.00005448515,0.0001055145,0.0001195711,0.00006468957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002897073,0.0004407704,0.02044173,0.003774426,0.001340116,0.001182036,0.0003765354,0.1028378,0.08669478,0.008915278,0.4995518,0.2715476],"study_design_scores_gemma":[0.0004462092,0.0003128885,0.009797117,0.0001957233,0.0002716774,0.0004981402,0.0001520453,0.728119,0.06777386,0.02609087,0.166128,0.0002144935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04690609,0.002796585,0.4152616,0.001116264,0.0006135532,0.0005938734,0.1715636,0.3529368,0.008211678],"genre_scores_gemma":[0.1443248,0.001503854,0.3374048,0.001597001,0.0001570203,0.001378603,0.481057,0.02169402,0.01088287],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01424564,"threshold_uncertainty_score":0.04765636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921561895975511,"score_gpt":0.2411476795087058,"score_spread":0.2219320605489507,"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."}}