{"id":"W4210770764","doi":"10.1002/alz.054715","title":"Effective feature learning of multi‐modal genetic and neuroimaging data for prediction of future conversion to Alzheimer’s disease: A machine learning based study","year":2021,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feature selection; Neuroimaging; Discriminative model; Artificial intelligence; Single-nucleotide polymorphism; Dementia; Classifier (UML); Computer science; Imaging genetics; Machine learning; SNP; Pattern recognition (psychology); Disease; Medicine; Psychology; Biology; Genetics; Neuroscience; Pathology; Genotype; Gene","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":[0.006317911,0.0008294429,0.0009568066,0.002097971,0.0005253021,0.001119765,0.0008577769,0.001148344,0.0007754678],"category_scores_gemma":[0.00984497,0.0002087204,0.001485186,0.001118622,0.0006134342,0.0008589833,0.000639818,0.001188636,0.0002142919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004791727,"about_ca_system_score_gemma":0.0007739954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003279289,"about_ca_topic_score_gemma":0.001812495,"domain_scores_codex":[0.9983798,0.0009146077,0.0001082345,0.0002761245,0.0001578248,0.0001634906],"domain_scores_gemma":[0.9884143,0.009386487,0.0004645624,0.000684282,0.0007167215,0.0003335983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002661752,0.004172248,0.5411736,0.000220809,0.001919442,0.0007062539,0.0003172616,0.1611362,0.006127815,0.0014457,0.004122186,0.2759968],"study_design_scores_gemma":[0.00003207317,0.000335102,0.04534991,0.00001548925,0.0001081303,0.0001064907,0.00007700486,0.9523572,0.0007187881,0.0007256726,0.0001523173,0.00002174513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533522,0.0008518035,0.04424701,0.000486759,0.00006124644,0.00007982056,0.0003165375,0.0001669011,0.0004377538],"genre_scores_gemma":[0.9909132,0.00009266692,0.008255761,0.00004872337,0.00004646221,0.00002983253,0.0004053659,0.00001054946,0.0001975317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006317911,"threshold_uncertainty_score":0.03341269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572031902248898,"score_gpt":0.2588576271875965,"score_spread":0.2431373081651075,"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."}}