{"id":"W4296780664","doi":"10.1093/bioinformatics/btac478","title":"GNN-SubNet: disease subnetwork detection with explainable graph neural networks","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Austrian Science Fund; European Commission","keywords":"Subnetwork; Subnet; Interpretability; Computer science; Python (programming language); Graph; Machine learning; Artificial intelligence; Data mining; Theoretical computer science; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002581146,0.0002825677,0.0001926944,0.00008576865,0.0006834901,0.00008929167,0.0003554573,0.0000957313,0.0000564503],"category_scores_gemma":[0.00001069283,0.0002558877,0.000129649,0.0003405546,0.00009346227,0.00002327604,0.0003461092,0.0003002707,0.000007570255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004772078,"about_ca_system_score_gemma":0.00007349462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009360194,"about_ca_topic_score_gemma":0.00002610114,"domain_scores_codex":[0.9984175,0.00005065007,0.0004489618,0.000217574,0.0002993082,0.00056602],"domain_scores_gemma":[0.9988377,0.00001400438,0.0002603433,0.0005750777,0.00006461415,0.0002482324],"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":[0.001495444,0.0001772028,0.002636941,0.000150601,0.0002310194,0.00002536355,0.0004772428,0.9246007,0.0004153911,0.0004563916,0.01967558,0.0496581],"study_design_scores_gemma":[0.001311937,0.001029568,0.001124462,0.00001329598,0.0000789434,0.0001164578,0.0009597778,0.9099409,0.0002424683,0.0002093135,0.08421799,0.0007549047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4287749,0.003093586,0.5575492,0.0002663759,0.002004638,0.001829889,0.0001663688,0.0002213988,0.006093632],"genre_scores_gemma":[0.9955302,0.0001048894,0.001787586,0.00104004,0.0003574875,0.0001465359,0.0005431969,0.00004256797,0.0004474506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5667553,"threshold_uncertainty_score":0.9999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005619933380862117,"score_gpt":0.188813731602062,"score_spread":0.1831937982211999,"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."}}