{"id":"W2079176280","doi":"10.1158/1535-7163.targ-11-a2","title":"Abstract A2: Development of a phenotypic profiling platform with high predictive value for the identification of novel antiangiogenic drugs.","year":2011,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Angiogenesis and VEGF in Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Angiogenesis; High-content screening; Phenotypic screening; Drug discovery; In vivo; Drug development; Receptor tyrosine kinase; Pharmacology; Drug; Cancer research; Small molecule; Medicine; Computational biology; Chemistry; Biology; Receptor; Phenotype; Bioinformatics; Biochemistry; Cell; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002097393,0.0001523134,0.0001527882,0.00003156624,0.00007653895,0.000006168568,0.0002380601,0.0000985256,0.000006241512],"category_scores_gemma":[0.000004008331,0.0001078959,0.0001209868,0.0001018075,0.0001135338,0.000005028845,0.00003695055,0.00004062595,2.496013e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002671391,"about_ca_system_score_gemma":0.0002721472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000042945,"about_ca_topic_score_gemma":0.00001669358,"domain_scores_codex":[0.9990303,0.000008864376,0.0003638998,0.000241485,0.000176686,0.0001787783],"domain_scores_gemma":[0.9990708,0.00001217977,0.0003316193,0.0003174292,0.0002428682,0.00002515422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005556137,0.0001234253,0.0005891789,0.00007309059,0.0009745531,1.524433e-7,0.0008402898,0.0008258414,0.9867254,0.0006666816,0.000003799392,0.008621995],"study_design_scores_gemma":[0.0007098714,0.0001409967,0.006616142,0.00003571683,0.0002180993,7.955592e-7,0.0002587267,0.0003379398,0.990718,0.00004811082,0.0007718319,0.0001437644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7633739,0.004683308,0.2310917,0.00001910008,0.0001070284,0.0005433843,0.00009716088,0.000006009345,0.00007840107],"genre_scores_gemma":[0.9894521,0.0002302343,0.009860094,0.0001039864,0.00004996501,0.0001973667,0.00004657911,0.00003599497,0.00002365174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2260782,"threshold_uncertainty_score":0.4399867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03656891870939759,"score_gpt":0.2752022129019118,"score_spread":0.2386332941925142,"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."}}