{"id":"W3178052844","doi":"10.1038/s42003-021-02393-7","title":"Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade","year":2021,"lang":"en","type":"article","venue":"Communications Biology","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"Immunotherapy; Blockade; Computational biology; Systems biology; Drug; Clinical trial; Immune system; Machine learning; Bioinformatics; Medicine; Biology; Artificial intelligence; Oncology; Computer science; Immunology; Pharmacology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000431955,0.0001810296,0.0002490754,0.00007283068,0.0003507434,0.00005046096,0.0005594068,0.0003179351,0.000002940315],"category_scores_gemma":[0.0006857471,0.0001700644,0.00009475047,0.0001502922,0.0002705825,0.00001037672,0.0007633839,0.0001701165,0.000001297342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000222089,"about_ca_system_score_gemma":0.0001593078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001321665,"about_ca_topic_score_gemma":0.00003269776,"domain_scores_codex":[0.9987416,0.0001949388,0.0004966598,0.0002403554,0.00002769746,0.0002987985],"domain_scores_gemma":[0.9977846,0.0003074284,0.0001913055,0.001356518,0.0002861612,0.0000739533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001733751,0.002073812,0.02174047,0.0003161439,0.00142983,8.720609e-7,0.0009080631,0.00252389,0.7904047,0.1610811,0.00446049,0.01332689],"study_design_scores_gemma":[0.007490888,0.001606545,0.008208673,0.00005162761,0.0001532244,0.000330986,0.001693255,0.5301788,0.009937041,0.001009315,0.4384854,0.0008542082],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8463987,0.02457991,0.1168401,0.007457365,0.0007793065,0.00161499,0.0006201958,0.00009432356,0.001615115],"genre_scores_gemma":[0.9896892,0.001633091,0.004392459,0.0002411613,0.00007295393,0.0003259892,0.00335789,0.00001980331,0.000267506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7804676,"threshold_uncertainty_score":0.6935027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03633672626772574,"score_gpt":0.2992059133512978,"score_spread":0.2628691870835721,"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."}}