{"id":"W4414296106","doi":"10.21203/rs.3.rs-7576397/v1","title":"HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Cognition; Biomarker; Cognitive decline; Deep learning; Biological data; Artificial neural network; Biological network; Disease","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":[0.001271377,0.001061879,0.00051361,0.0007942016,0.0004044907,0.0007794994,0.0009459345,0.001174608,0.007501068],"category_scores_gemma":[0.00341662,0.0003658515,0.000704691,0.0006167073,0.0002699286,0.000763516,0.001144232,0.001018549,0.001345117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007040006,"about_ca_system_score_gemma":0.001085687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00741889,"about_ca_topic_score_gemma":0.009465416,"domain_scores_codex":[0.999737,0.00007001373,0.000009657026,0.0000936873,0.00004652472,0.00004318018],"domain_scores_gemma":[0.9994586,0.0002855644,0.00002880967,0.00008222833,0.0001023168,0.00004245434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001130619,0.000473585,0.02210091,0.0004662329,0.0007419283,0.0004857751,0.0001605383,0.3614578,0.01480978,0.01363291,0.06190372,0.5226362],"study_design_scores_gemma":[0.00004756817,0.00005998346,0.004146304,0.00002409923,0.00005931908,0.00005004775,0.00001988667,0.9759409,0.003401622,0.01438997,0.001845018,0.00001539698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1308112,0.001284109,0.8289116,0.002466183,0.0006184484,0.0002414501,0.01451784,0.01360243,0.007546645],"genre_scores_gemma":[0.6850438,0.0004497442,0.2770683,0.0006603472,0.0002572512,0.0004338665,0.01432629,0.0009421468,0.02081827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007501068,"threshold_uncertainty_score":0.02509356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05574687828010769,"score_gpt":0.3915676068124634,"score_spread":0.3358207285323557,"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."}}