{"id":"W2886919764","doi":"10.1186/s12859-019-2737-1","title":"Multiscale modeling reveals angiogenesis-induced drug resistance in brain tumors and predicts a synergistic drug combination targeting EGFR and VEGFR pathways","year":2019,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Guangdong Province Key Laboratory of Computational Science; Sun Yat-sen University; National Natural Science Foundation of China; York University","keywords":"Angiogenesis; Drug; Drug resistance; VEGF receptors; Cancer research; EGFR inhibitors; DNA microarray; Computational biology; Epidermal growth factor receptor; Biology; Medicine; Bioinformatics; Pharmacology; Receptor; Genetics; Gene; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001933402,0.0003590806,0.0006784103,0.0002405817,0.0001308771,0.00008192623,0.0002378087,0.0001636315,0.00001225131],"category_scores_gemma":[0.002153331,0.0003218047,0.00007178801,0.0002842784,0.00009322932,0.0003722393,0.00021672,0.0002647423,0.00002813484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009635845,"about_ca_system_score_gemma":0.00006392627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001324395,"about_ca_topic_score_gemma":0.0001047278,"domain_scores_codex":[0.9973616,0.0001799707,0.001233132,0.0003376047,0.0003678057,0.0005199159],"domain_scores_gemma":[0.9971978,0.001672782,0.0004289186,0.0004271822,0.0001027409,0.0001705402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005139313,0.001410766,0.08050105,0.05235748,0.0002360145,0.00007886625,0.08589008,0.0008760048,0.01926578,0.749287,0.008225782,0.001357272],"study_design_scores_gemma":[0.001950437,0.00004718595,0.001299633,0.001188825,0.00004125144,0.00001499926,0.002469198,0.8814883,0.000605582,0.1103495,0.00001467649,0.0005303926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847664,0.0002390413,0.0116065,0.000195917,0.00008672151,0.001063054,0.00003293305,0.0001441684,0.001865257],"genre_scores_gemma":[0.856191,0.00001232626,0.1431859,0.0001016633,0.00002317195,0.00004516485,0.00003151868,0.00003953786,0.0003697562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8806123,"threshold_uncertainty_score":0.9999234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02685056877577661,"score_gpt":0.2511634707739949,"score_spread":0.2243129019982183,"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."}}