{"id":"W2982908687","doi":"10.1101/840553","title":"BDKANN - Biological Domain Knowledge-based Artificial Neural Network for drug response prediction","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Leverage (statistics); Computer science; Drug response; Machine learning; Artificial intelligence; Domain knowledge; Artificial neural network; Biological network; Computational biology; Drug; Biology; Pharmacology","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":[0.0006943346,0.0007252739,0.0005132102,0.0008010654,0.0003047199,0.0007213249,0.00118158,0.001052268,0.008030892],"category_scores_gemma":[0.002538463,0.0003435549,0.0005252886,0.0008107036,0.0002552083,0.0007370727,0.0007281611,0.001300759,0.002581492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020307,"about_ca_system_score_gemma":0.001350663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00806283,"about_ca_topic_score_gemma":0.01266254,"domain_scores_codex":[0.9997635,0.00005918546,0.00001335986,0.00006701873,0.00007196934,0.00002495129],"domain_scores_gemma":[0.9994627,0.0002667018,0.00003928715,0.00006443015,0.0001370264,0.00002987852],"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":[0.00046754,0.0002519183,0.003788082,0.000581203,0.0001916914,0.0001411365,0.00003989016,0.6007397,0.005405249,0.01052186,0.07502606,0.3028456],"study_design_scores_gemma":[0.00001843847,0.00001322179,0.0002397046,0.00001562848,0.00000881388,0.00001295878,0.000003637962,0.9906317,0.001159962,0.005081867,0.002808307,0.000005748421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09366395,0.006466336,0.7909865,0.005299868,0.001014864,0.0004487153,0.03554526,0.03393352,0.03264093],"genre_scores_gemma":[0.5237666,0.002072953,0.4092894,0.001207422,0.0002423304,0.0007814663,0.0407946,0.0007959284,0.02104932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00806283,"threshold_uncertainty_score":0.02686602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03345837556856165,"score_gpt":0.2750085750202364,"score_spread":0.2415501994516747,"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."}}