{"id":"W4303699434","doi":"10.3390/ijms231911624","title":"Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction","year":2022,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"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; Biology; Computer science; Immune system; 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":[0.001193711,0.002767847,0.001262495,0.001415602,0.0006137693,0.001503163,0.001593367,0.001077734,0.01385308],"category_scores_gemma":[0.002997663,0.0009472471,0.002742511,0.0008460202,0.0003399113,0.00111502,0.001408595,0.001669505,0.006374856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008697875,"about_ca_system_score_gemma":0.002158048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005438401,"about_ca_topic_score_gemma":0.01012997,"domain_scores_codex":[0.9995114,0.00006180294,0.00003956227,0.0002144097,0.0001071117,0.00006572061],"domain_scores_gemma":[0.9993721,0.0003233305,0.00006030919,0.00007281625,0.0001132478,0.00005828217],"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":[0.003703722,0.0005975894,0.03280565,0.005671573,0.00216192,0.002009836,0.0005845933,0.1182384,0.1042669,0.007722828,0.3934696,0.3287674],"study_design_scores_gemma":[0.0004856439,0.0004914533,0.01204984,0.000287395,0.0004535778,0.0007778142,0.0002177837,0.7475044,0.06381044,0.02206682,0.1515889,0.0002659914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0613304,0.004552779,0.4437465,0.001202394,0.0007054593,0.0007959456,0.1672303,0.3116297,0.008806585],"genre_scores_gemma":[0.1611521,0.00239343,0.3659966,0.001914212,0.0001546469,0.001673728,0.4371957,0.01878454,0.01073501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01385308,"threshold_uncertainty_score":0.04634315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240523062275705,"score_gpt":0.2938734854064672,"score_spread":0.2714682547837102,"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."}}