{"id":"W4388706662","doi":"10.1093/bioinformatics/btad679","title":"ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Public Health Ontario; University of Toronto; University Health Network","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence; Correlation; Machine learning; Robustness (evolution); Artificial neural network; Canonical correlation; Generalizability theory; Convolutional neural network; Novelty; Gene; Biology; Mathematics; Genetics; Statistics","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":[0.0005660892,0.0007131369,0.0005361634,0.0006252999,0.0001865634,0.0004541525,0.001175004,0.0007070181,0.002136209],"category_scores_gemma":[0.001447772,0.0002884686,0.0005398273,0.0004807381,0.0003187347,0.0006491675,0.0006419784,0.00107703,0.0006380085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000896777,"about_ca_system_score_gemma":0.001439506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01428057,"about_ca_topic_score_gemma":0.01822583,"domain_scores_codex":[0.9998547,0.00002863156,0.000007012948,0.0000516755,0.00003741881,0.00002055711],"domain_scores_gemma":[0.9997218,0.0001071547,0.00003277384,0.00003897889,0.00007632685,0.00002294265],"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":[0.000206685,0.0001474368,0.005776163,0.0001153747,0.0001433038,0.0001760849,0.00003433707,0.8225325,0.003699559,0.005193587,0.01074511,0.1512299],"study_design_scores_gemma":[0.000007575319,0.00001062857,0.0001723571,0.000005376185,0.000006085918,0.00001279495,0.000001609311,0.9965975,0.0007647329,0.00198701,0.000431038,0.000003395753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1083102,0.001802751,0.8657306,0.001285262,0.0001884218,0.0001514595,0.003678758,0.01416823,0.004684377],"genre_scores_gemma":[0.7210708,0.000836635,0.2601956,0.0007016488,0.00009080751,0.0003029776,0.008221326,0.000424885,0.00815531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01428057,"threshold_uncertainty_score":0.02839488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467302902274496,"score_gpt":0.2301602004368411,"score_spread":0.2154871714140962,"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."}}