{"id":"W3037207689","doi":"10.1093/bioinformatics/btaa596","title":"DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computational biology; Prioritization; Genome; Web server; ENCODE; Immunogenicity; Source code; Deep learning; Artificial intelligence; Machine learning; Biology; The Internet; Genetics; Antigen; Gene","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":[0.0004273934,0.0009350201,0.0008511471,0.0008752893,0.0003477578,0.0006303375,0.001703304,0.001122646,0.002658978],"category_scores_gemma":[0.001030197,0.0005121842,0.001017113,0.0007118715,0.0004255242,0.001054167,0.001221053,0.001394817,0.0006417365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007722497,"about_ca_system_score_gemma":0.001324218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008503411,"about_ca_topic_score_gemma":0.01092411,"domain_scores_codex":[0.9997799,0.00003134315,0.00001186441,0.00005849476,0.00007775606,0.00004065903],"domain_scores_gemma":[0.9997398,0.00007608476,0.00004169594,0.00003425402,0.00007513828,0.00003305206],"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":[0.0001663642,0.0001304874,0.003401268,0.000183044,0.0001275284,0.0002628217,0.0001025236,0.5704904,0.02152388,0.01031427,0.01226145,0.381036],"study_design_scores_gemma":[0.000006514182,0.00001115208,0.00008497889,0.000003180465,0.000005516743,0.00002488421,0.000005420942,0.9953421,0.001447513,0.002372846,0.0006915885,0.00000424057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01540409,0.000237481,0.9804168,0.0002379149,0.0000527846,0.00004567791,0.0002786647,0.002165199,0.001161383],"genre_scores_gemma":[0.3589128,0.0005030537,0.6302879,0.0007966486,0.0001106145,0.0002466834,0.002399035,0.0004987461,0.006244594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008503411,"threshold_uncertainty_score":0.01690781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133889510537646,"score_gpt":0.2481130135724254,"score_spread":0.226774118467049,"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."}}