{"id":"W6920322876","doi":"10.60692/db2c2-aj165","title":"Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Angiogenesis; Tumor cells; Endothelial stem cell; Cell growth; Chemotherapy; Reinforcement learning; Vascular endothelial growth factor","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":[0.0002623716,0.0005024873,0.0004854206,0.0003269381,0.000234178,0.0005769336,0.0004212407,0.0007714233,0.0007725426],"category_scores_gemma":[0.0008447205,0.0002920183,0.0007296768,0.0002622318,0.0003116392,0.0003681682,0.0002899499,0.0003704232,0.0001169532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006581016,"about_ca_system_score_gemma":0.0008500913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008422908,"about_ca_topic_score_gemma":0.005179091,"domain_scores_codex":[0.9998934,0.0000334551,0.000007286142,0.0000181219,0.00003380368,0.00001392101],"domain_scores_gemma":[0.9996934,0.0001974137,0.0000484373,0.00001707689,0.00003288308,0.00001091067],"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":[0.000008310157,0.000008763934,0.0002913308,0.000009768421,0.000005058027,0.000009020968,0.000003850334,0.9970731,0.0006994638,0.0003255375,0.00002607689,0.001539758],"study_design_scores_gemma":[0.00000120674,0.000005904418,0.0000605105,9.412993e-7,0.000002050408,0.000002396816,9.817768e-7,0.9993637,0.0003661722,0.0001232646,0.00007171245,0.000001220013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.389275,0.001078792,0.5977973,0.0005697036,0.00008727861,0.0001341344,0.0005069515,0.0005982737,0.009952504],"genre_scores_gemma":[0.9615216,0.0005855795,0.03590123,0.00005902586,0.00001311094,0.0001598281,0.0001800999,0.00002810688,0.001551402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008422908,"threshold_uncertainty_score":0.01674777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07303140162977811,"score_gpt":0.2680069900201054,"score_spread":0.1949755883903273,"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."}}