{"id":"W4293794829","doi":"10.1109/cibcb55180.2022.9863043","title":"geneDRAGNN: Gene Disease Prioritization using Graph Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Gene regulatory network; Disease; Computer science; Computational biology; Gene; Artificial neural network; Machine learning; Artificial intelligence; Bioinformatics; Biology; Genetics; Gene expression; Medicine; Pathology","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":[0.001021898,0.0006800703,0.0006034474,0.002894363,0.0003713412,0.0008252642,0.0008715513,0.0006148865,0.002876818],"category_scores_gemma":[0.003396265,0.0002454062,0.0007007562,0.001549826,0.0002461235,0.0006283167,0.0007032693,0.0005854557,0.0005051796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008530039,"about_ca_system_score_gemma":0.00103763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008421196,"about_ca_topic_score_gemma":0.01521725,"domain_scores_codex":[0.9996232,0.0001386163,0.00001987217,0.000105404,0.00008760674,0.00002527399],"domain_scores_gemma":[0.9990352,0.0006523246,0.00008443455,0.00006952885,0.0001224254,0.00003600332],"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":[0.0005547506,0.0003081331,0.01851952,0.000806848,0.0004678022,0.0004703978,0.0001218263,0.4676584,0.008457198,0.01425154,0.02347275,0.4649108],"study_design_scores_gemma":[0.00004047358,0.00004163131,0.001651743,0.00002285883,0.00005183962,0.00008850054,0.00001994311,0.9798949,0.001808769,0.01262209,0.003745537,0.00001169975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1121958,0.00306929,0.8513093,0.002133467,0.0002269395,0.0005183287,0.01190457,0.01177998,0.006862223],"genre_scores_gemma":[0.4621729,0.001157543,0.5189372,0.0005795733,0.0001311073,0.0004522114,0.01184678,0.0003823435,0.004340391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008421196,"threshold_uncertainty_score":0.01674432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008636403172651323,"score_gpt":0.2189893566492806,"score_spread":0.2103529534766292,"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."}}