{"id":"W2959991108","doi":"10.3389/fgene.2019.00726","title":"Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer’s Disease","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; 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; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Engineering and Physical Sciences Research Council; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Autoencoder; Machine learning; Expression quantitative trait loci; Classifier (UML); Computational biology; Computer science; Deep learning; Gene regulatory network; Biology; Unsupervised learning; Feature selection; Gene; Gene expression; Genetics; Single-nucleotide polymorphism; 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":[0.001443438,0.0005970863,0.0004441517,0.0009594411,0.0001348766,0.0004982706,0.0003314571,0.0003679124,0.0004908125],"category_scores_gemma":[0.002037084,0.0002063186,0.0008510266,0.0005978186,0.0001688077,0.000310353,0.0004299657,0.0007089215,0.0001703657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002660466,"about_ca_system_score_gemma":0.000413549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001892395,"about_ca_topic_score_gemma":0.003553291,"domain_scores_codex":[0.9996113,0.0001825216,0.00002454823,0.00009352065,0.00004854342,0.0000396795],"domain_scores_gemma":[0.9991953,0.0005399913,0.00008103049,0.00008745541,0.00007323678,0.00002308105],"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":[0.0009399763,0.0005587583,0.07568076,0.0001870086,0.0009996886,0.000366895,0.0002431415,0.4516309,0.07494087,0.003437363,0.0009016345,0.3901129],"study_design_scores_gemma":[0.000009556114,0.00007228211,0.01400388,0.000009092482,0.00004926458,0.00005634601,0.00002357653,0.9781675,0.004837518,0.002494591,0.0002602207,0.00001623465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4821945,0.0007952337,0.5143673,0.000216923,0.00004127621,0.00004762948,0.0006311653,0.0009136757,0.0007923241],"genre_scores_gemma":[0.881862,0.0002166015,0.1160753,0.00006912115,0.00002526557,0.00006325119,0.001001623,0.0000625107,0.0006244856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001892395,"threshold_uncertainty_score":0.007633686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009620796184804164,"score_gpt":0.2727425707145507,"score_spread":0.2631217745297465,"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."}}