{"id":"W2888151108","doi":"10.1101/397760","title":"A 3-fold kernel approach for characterizing Late Onset Alzheimer’s Disease","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":1,"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; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; Biogen; BioClinica; Eli Lilly and Company; Bristol-Myers Squibb; U.S. Department of Defense; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"SNP; Single-nucleotide polymorphism; Computational biology; Genome-wide association study; Biology; Kernel (algebra); Gene; Genetics; Computer science; Mathematics","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":[0.002123319,0.0005552492,0.0006898104,0.001465157,0.0003388205,0.001044642,0.0005312759,0.0006744012,0.00117187],"category_scores_gemma":[0.003491,0.0001557351,0.001059378,0.0008562543,0.0003280293,0.0005861904,0.0008453827,0.0007080339,0.0007440648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005125414,"about_ca_system_score_gemma":0.0008084013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001982735,"about_ca_topic_score_gemma":0.00129409,"domain_scores_codex":[0.9992586,0.0002459527,0.00005200752,0.0001759162,0.0001469724,0.0001204783],"domain_scores_gemma":[0.9985675,0.0004839045,0.0002027346,0.0003198455,0.0003279342,0.00009808309],"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":[0.004103747,0.0009932265,0.1243659,0.0003712293,0.001314284,0.0005343035,0.0005833198,0.1896289,0.1257782,0.01314308,0.008648061,0.5305359],"study_design_scores_gemma":[0.00003764069,0.0002200958,0.03979586,0.00001656857,0.0001036637,0.0003073901,0.00008559245,0.9322216,0.01333063,0.01175297,0.002077636,0.00005029494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3735616,0.0004416607,0.6221057,0.0002432327,0.00003996928,0.00008577437,0.001158438,0.00149981,0.0008637258],"genre_scores_gemma":[0.8999584,0.00009906416,0.09698185,0.00004541981,0.00002354201,0.00007807691,0.001561717,0.0001181855,0.00113373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002123319,"threshold_uncertainty_score":0.01122934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04322252380977948,"score_gpt":0.2870602741903675,"score_spread":0.243837750380588,"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."}}