{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000328156,0.0005412525,0.0005019713,0.0001180964,0.0003271051,0.0001221672,0.0006479895,0.0004862986,0.00006886831],"category_scores_gemma":[0.0000806774,0.0005911667,0.0003503784,0.0001838658,0.00005985167,0.00001244202,0.00105941,0.000487255,0.000006660888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001490162,"about_ca_system_score_gemma":0.0006066065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000980331,"about_ca_topic_score_gemma":0.000003844849,"domain_scores_codex":[0.9977037,0.00007789859,0.0006565554,0.0007914152,0.000252909,0.0005175751],"domain_scores_gemma":[0.9976681,0.00001834674,0.0005071486,0.001239815,0.0004357056,0.0001309074],"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":[0.0002205727,0.0001198902,0.001243664,0.0004355509,0.0004879037,0.000001864849,0.00001459671,0.001682451,0.9915527,0.00005752738,0.004172034,0.00001122851],"study_design_scores_gemma":[0.001656651,0.0002535387,0.01517769,0.0001226907,0.0003131337,7.211794e-8,0.00003248238,0.00874284,0.5740953,0.000002235035,0.3985033,0.001100038],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704111,0.01062368,0.0114099,0.0001781145,0.001955104,0.001847464,0.003447001,0.0001155419,0.00001204064],"genre_scores_gemma":[0.9894322,0.003302192,0.003712928,0.0004252444,0.001086025,0.001724622,0.00004965207,0.0001802152,0.00008698192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4174574,"threshold_uncertainty_score":0.999654,"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."}}