{"id":"W3217326089","doi":"10.1101/2021.11.29.470386","title":"Graph Transformer for drug response prediction","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Feature learning; Graph; Redundancy (engineering); Artificial intelligence; Machine learning; Convolutional neural network; External Data Representation; Transformer; Representation (politics); Data mining; Pattern recognition (psychology); Theoretical computer science","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":[0.0004071366,0.0009026569,0.0006832038,0.001491278,0.0001730489,0.0005875847,0.0008329442,0.0008348587,0.00576112],"category_scores_gemma":[0.002380713,0.0002931422,0.0009261863,0.001027298,0.0003625837,0.001089642,0.0005010596,0.0009659087,0.001229323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116625,"about_ca_system_score_gemma":0.000960205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005314131,"about_ca_topic_score_gemma":0.005335851,"domain_scores_codex":[0.9997516,0.00006169613,0.00001145883,0.0000857362,0.00006155449,0.00002802807],"domain_scores_gemma":[0.99921,0.0004633311,0.00008164106,0.00007942234,0.0001264629,0.00003917622],"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":[0.0003492529,0.0002078075,0.003440503,0.000327299,0.0001265664,0.000179548,0.00002447302,0.7306651,0.005414507,0.01424376,0.01238438,0.2326367],"study_design_scores_gemma":[0.000006922043,0.00002573443,0.0002357232,0.000006643798,0.00001129716,0.00002287772,0.000001945903,0.9874939,0.001002355,0.01034556,0.0008431927,0.000003891384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08492582,0.002874731,0.8830522,0.002151269,0.0002008184,0.000224545,0.007480124,0.009507934,0.009582534],"genre_scores_gemma":[0.8648446,0.00135296,0.1186199,0.0005897206,0.0001274182,0.0001995881,0.006835868,0.0002901236,0.007139769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00576112,"threshold_uncertainty_score":0.01927286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01756449919057424,"score_gpt":0.252396129516913,"score_spread":0.2348316303263388,"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."}}