{"id":"W4206812377","doi":"10.1016/j.apm.2021.12.033","title":"A novel numerical and artificial intelligence based approach to study anti-angiogenic drugs: Endostatin","year":2022,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Angiogenesis and VEGF in Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Endostatin; Angiogenesis; Endogeny; Cancer research; Normalization (sociology); Cancer; Drug delivery; Computer science; Medicine; Pharmacology; Internal medicine; Chemistry","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":[0.0003732017,0.0005929524,0.000680613,0.0006968719,0.0003686183,0.001185069,0.001011768,0.001366212,0.001712889],"category_scores_gemma":[0.0008495807,0.0002779947,0.0008759717,0.0005541848,0.0007777542,0.000916781,0.0007293244,0.000844638,0.0003023467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006519284,"about_ca_system_score_gemma":0.0007700631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002438501,"about_ca_topic_score_gemma":0.001621174,"domain_scores_codex":[0.9998221,0.00004534672,0.00001338445,0.00003161578,0.00007403202,0.00001364353],"domain_scores_gemma":[0.9996817,0.0001579591,0.00004567401,0.00002278585,0.00007096244,0.00002096042],"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":[0.00003087734,0.00009771981,0.000595731,0.0001731169,0.00007458311,0.0001612163,0.00006486564,0.8719717,0.01413056,0.08928396,0.0005726873,0.02284312],"study_design_scores_gemma":[0.00000420467,0.00001538893,0.00006020232,0.000004700903,0.0000073418,0.00003023303,0.000004799242,0.991356,0.000806901,0.006486625,0.001218544,0.0000051602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0152234,0.0005848487,0.9749267,0.0004776646,0.0001513869,0.00004611151,0.00006494708,0.0001280593,0.008396949],"genre_scores_gemma":[0.4455545,0.001799852,0.5348505,0.0003362309,0.0002107668,0.0003072022,0.0001629315,0.00009074248,0.01668722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002438501,"threshold_uncertainty_score":0.005730212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04358534233859772,"score_gpt":0.2754828848679656,"score_spread":0.2318975425293678,"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."}}