{"id":"W4387610056","doi":"10.1186/s12920-023-01675-9","title":"c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer’s disease","year":2023,"lang":"en","type":"article","venue":"BMC Medical Genomics","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Khalifa University of Science, Technology and Research; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"KEGG; Disease; Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Neuropsychology; Biomarker; Alzheimer's disease; Single-nucleotide polymorphism; Medicine; Neurology; Deep learning; Computational biology; Interpretability; Bioinformatics; Machine learning; Artificial intelligence; Oncology; Cognition; Biology; Computer science; Pathology; Gene; Genetics; Transcriptome; Psychiatry; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006929929,0.0008560756,0.0006538573,0.0004667399,0.0002880874,0.0005774233,0.001730662,0.001289341,0.001793171],"category_scores_gemma":[0.001916453,0.0003753237,0.0008122366,0.0004208973,0.0004615271,0.000580496,0.000965277,0.001582562,0.0002530951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009650835,"about_ca_system_score_gemma":0.001441939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182989,"about_ca_topic_score_gemma":0.01277085,"domain_scores_codex":[0.9997849,0.0000714625,0.00001105935,0.00006645562,0.00003285208,0.00003325709],"domain_scores_gemma":[0.9995257,0.0002749402,0.00003814388,0.00002663641,0.00009640085,0.00003828371],"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":[0.0002070152,0.0001197145,0.003351726,0.00008532897,0.0001326082,0.0001569138,0.00005176115,0.9240353,0.001498447,0.008138915,0.00475469,0.0574677],"study_design_scores_gemma":[0.000007404993,0.00001243632,0.0000979506,0.00000394124,0.000005709342,0.000008057653,0.00000144207,0.9971359,0.0001290073,0.002404101,0.0001916354,0.000002477319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1273473,0.001800871,0.8603424,0.002097027,0.0001929323,0.0001492949,0.001876503,0.001429966,0.004763653],"genre_scores_gemma":[0.852416,0.0005707188,0.1372198,0.0009730332,0.00007522643,0.0003250848,0.001960719,0.00006831284,0.006391165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01182989,"threshold_uncertainty_score":0.02352208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08459575549839929,"score_gpt":0.3748144574533672,"score_spread":0.2902187019549679,"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."}}