{"id":"W4210607727","doi":"10.1093/bioinformatics/btac077","title":"PDMDA: predicting deep-level miRNA–disease associations with graph neural networks and sequence features","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; Higher Education Discipline Innovation Project; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Softmax function; Computer science; Artificial neural network; Artificial intelligence; Disease; Computational biology; Feature (linguistics); Association (psychology); microRNA; Machine learning; Biology; Genetics; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001409838,0.000123642,0.0000846992,0.00004395409,0.000348705,0.00004859845,0.0001227502,0.00004850058,0.000007534916],"category_scores_gemma":[0.00007023073,0.000117194,0.00004278154,0.0001325222,0.00005750108,0.00001514493,0.0001639061,0.0001121502,3.798659e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003236734,"about_ca_system_score_gemma":0.00006034842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007111724,"about_ca_topic_score_gemma":0.00001561491,"domain_scores_codex":[0.9992459,0.00004531509,0.0001862599,0.0001350749,0.000189773,0.0001976446],"domain_scores_gemma":[0.9994196,0.00001628464,0.0001682398,0.0002151264,0.00005776386,0.0001229558],"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":[0.0009019188,0.00042142,0.3626423,0.0004882049,0.0008186467,0.00003007226,0.001991543,0.5599649,0.01815775,0.001653399,0.01634409,0.0365858],"study_design_scores_gemma":[0.00129825,0.0003413595,0.3366977,0.0000228459,0.0001785011,0.00008490982,0.000731858,0.6566734,0.0006246722,0.000147284,0.00260364,0.0005955572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805398,0.001033187,0.01690321,0.000187723,0.0001456072,0.0003963336,0.0004090768,0.00004739586,0.0003376718],"genre_scores_gemma":[0.9951596,0.00002900627,0.003364862,0.0002948734,0.0000857916,0.00003973554,0.0009310007,0.00001563383,0.00007945173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0967085,"threshold_uncertainty_score":0.4779032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399618774769522,"score_gpt":0.2317315699094358,"score_spread":0.2177353821617406,"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."}}