{"id":"W4296908338","doi":"10.1101/2022.09.21.508954","title":"Predicting Phenotypes From Novel Genomic Markers Using Deep Learning","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Single-nucleotide polymorphism; Convolutional neural network; SNP; Computational biology; Biology; Pearson product-moment correlation coefficient; Genetics; Artificial intelligence; Genotype; Computer science; Statistics; Mathematics; 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":[0.0006663451,0.0008078588,0.0004424862,0.0006276485,0.0001231282,0.0005217136,0.0004583798,0.0005461986,0.0007713072],"category_scores_gemma":[0.001180126,0.0001898044,0.000461246,0.0005509823,0.0002493052,0.0004374503,0.0004628086,0.0008109682,0.000270635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004913663,"about_ca_system_score_gemma":0.0004019795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004254038,"about_ca_topic_score_gemma":0.004799998,"domain_scores_codex":[0.9997858,0.0000512128,0.00001014858,0.00008427988,0.00003931095,0.00002922479],"domain_scores_gemma":[0.9994329,0.000321428,0.00006642778,0.000052367,0.00009746027,0.00002932535],"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":[0.0002493722,0.0002791164,0.03294183,0.00009191546,0.0001834782,0.0001917039,0.00003742926,0.8128626,0.02605102,0.001472152,0.001805392,0.123834],"study_design_scores_gemma":[0.000004004354,0.00001595975,0.00223891,0.000004611701,0.000009360839,0.00001087165,0.000003817569,0.9937503,0.002669747,0.001130593,0.0001574625,0.000004386216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5968056,0.0009245244,0.3965094,0.0004783301,0.00007804221,0.00003719241,0.001883032,0.001770803,0.001513142],"genre_scores_gemma":[0.9589264,0.0001501515,0.03791248,0.0001081923,0.00001630811,0.00003025394,0.001645757,0.00003405619,0.001176304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004254038,"threshold_uncertainty_score":0.008458555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287128840828719,"score_gpt":0.2112163955819841,"score_spread":0.1983451071736969,"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."}}