{"id":"W3164648499","doi":"10.3390/cancers13112595","title":"GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network","year":2021,"lang":"en","type":"article","venue":"Cancers","topic":"Circular RNAs in diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Circular RNA; Computational biology; Graph; Computer science; microRNA; Non-coding RNA; Similarity (geometry); Disease; Entropy (arrow of time); Bioinformatics; Data mining; Biology; Artificial intelligence; Theoretical computer science; Medicine; Genetics; Gene; 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":[0.0006623701,0.001081832,0.0007166372,0.003364421,0.0005247804,0.0006080255,0.0007145808,0.0009747815,0.001544705],"category_scores_gemma":[0.00259984,0.0002541726,0.001123686,0.001269327,0.0003106792,0.0005830206,0.0007288192,0.0008471814,0.000231048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000979668,"about_ca_system_score_gemma":0.00108492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0170705,"about_ca_topic_score_gemma":0.02237247,"domain_scores_codex":[0.999633,0.00009803962,0.00002380358,0.0001309548,0.00006221409,0.00005205609],"domain_scores_gemma":[0.9985508,0.0009512843,0.0001467201,0.00006588608,0.0001925438,0.0000926338],"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":[0.0007586553,0.0005384937,0.1337972,0.0004517785,0.0009528263,0.0009262815,0.0001967113,0.4960377,0.01335944,0.005778192,0.01559919,0.3316036],"study_design_scores_gemma":[0.00001892811,0.00004788854,0.004881132,0.000009232311,0.0000641023,0.0001187744,0.00001801388,0.9905332,0.000837548,0.002582686,0.0008780789,0.00001040676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4211116,0.002322964,0.5607161,0.001144014,0.0002185253,0.0004958559,0.005117803,0.004889486,0.003983744],"genre_scores_gemma":[0.8808009,0.0005339648,0.110392,0.0003439153,0.0001195321,0.0002679724,0.005522747,0.00008831777,0.001930637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0170705,"threshold_uncertainty_score":0.03394228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021660540257745,"score_gpt":0.2501774455004701,"score_spread":0.2399608400978926,"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."}}