{"id":"W4224277513","doi":"10.1016/j.compbiomed.2022.105511","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":"Computers in Biology and Medicine","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Toronto Metropolitan University; BC Cancer Agency; University of Waterloo","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; European Commission","keywords":"Bevacizumab; Angiogenesis; Computer science; Vascular endothelial growth factor; Chemotherapy; Artificial intelligence; Machine learning; Medicine; VEGF receptors; Cancer research; Internal medicine","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":[0.0002775849,0.0005383731,0.0005564704,0.0003315083,0.0002361437,0.0005920093,0.0004824221,0.0008938445,0.0008000002],"category_scores_gemma":[0.0008699778,0.0003263642,0.0008202145,0.0002782702,0.0003392984,0.0003661361,0.0003001973,0.0004149432,0.0001183593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006550024,"about_ca_system_score_gemma":0.0008847037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008358643,"about_ca_topic_score_gemma":0.004683949,"domain_scores_codex":[0.9998839,0.00003681188,0.000007883061,0.00001964289,0.00003633606,0.00001545866],"domain_scores_gemma":[0.9996386,0.0002375392,0.00005358276,0.0000186737,0.00003851142,0.00001307584],"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":[0.000008706837,0.000009111017,0.0002560294,0.00001107555,0.000005285362,0.000009989858,0.000003724023,0.9974062,0.0006939284,0.0002718698,0.00002506879,0.001299129],"study_design_scores_gemma":[0.0000015767,0.00000690807,0.00005737249,0.000001044433,0.00000238954,0.000002815124,0.000001048311,0.999346,0.0003871929,0.0001203293,0.00007202529,0.000001365023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4199794,0.00127635,0.5666056,0.0006057976,0.0001018312,0.0001577682,0.0006019782,0.0006384789,0.01003275],"genre_scores_gemma":[0.9612262,0.0006627572,0.03613204,0.00007255618,0.00001497775,0.0001958036,0.000210251,0.00003264916,0.001452661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008358643,"threshold_uncertainty_score":0.01661998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06039283000072233,"score_gpt":0.3412944424365431,"score_spread":0.2809016124358207,"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."}}