{"id":"W4323816133","doi":"10.1093/bioadv/vbad028","title":"Predicting phenotypes from novel genomic markers using deep learning","year":2023,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Single-nucleotide polymorphism; Convolutional neural network; Biology; Computational biology; SNP; Pearson product-moment correlation coefficient; Genetics; Phenotype; DNA sequencing; Artificial intelligence; Genotype; Computer science; DNA; Statistics; Gene; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000532596,0.0008368145,0.0004100376,0.0006123199,0.0001319801,0.000459185,0.0005101967,0.0004888792,0.001018171],"category_scores_gemma":[0.0009403693,0.0001721095,0.000491426,0.0005193902,0.0001980905,0.0003871756,0.0004542373,0.0007748614,0.0003082716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005013613,"about_ca_system_score_gemma":0.0004770347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004786521,"about_ca_topic_score_gemma":0.006207034,"domain_scores_codex":[0.9998382,0.00003404199,0.0000092616,0.00006313523,0.00003125364,0.00002402359],"domain_scores_gemma":[0.9994969,0.0002778687,0.00005740344,0.00003827728,0.0001023693,0.00002716586],"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":[0.0002296969,0.0003513714,0.03473981,0.0001167039,0.0002158657,0.0001741463,0.00003656496,0.7547123,0.01918478,0.001181433,0.002264089,0.1867932],"study_design_scores_gemma":[0.000004305356,0.00001953439,0.002290075,0.000005594844,0.00001337861,0.00001137453,0.000003958431,0.9944102,0.002235333,0.0008227651,0.0001789479,0.000004600447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5847438,0.001119593,0.407235,0.0005244191,0.00008305607,0.00005405478,0.001935923,0.002156856,0.002147261],"genre_scores_gemma":[0.9600359,0.0001830846,0.03632152,0.0001243736,0.00001720092,0.0000430107,0.001783535,0.00003482694,0.001456574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004786521,"threshold_uncertainty_score":0.009517312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358583109972697,"score_gpt":0.240774630662151,"score_spread":0.2271887995624241,"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."}}