{"id":"W2086099578","doi":"10.1002/gepi.20041","title":"Identifying SNPs predictive of phenotype using random forests","year":2004,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":373,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; University of Lethbridge","funders":"","keywords":"Random forest; Phenotype; Single-nucleotide polymorphism; Computational biology; Biology; Genetics; Statistics; Evolutionary biology; Computer science; Artificial intelligence; Mathematics; Gene; Genotype","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":[0.009037538,0.001595758,0.002151436,0.003112796,0.0008981435,0.001125929,0.00195543,0.001543177,0.001759695],"category_scores_gemma":[0.01424691,0.0008799352,0.002838111,0.001528675,0.000713611,0.0009843558,0.00098662,0.001735272,0.001047255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004924778,"about_ca_system_score_gemma":0.001107091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006035447,"about_ca_topic_score_gemma":0.007054931,"domain_scores_codex":[0.9966158,0.001921513,0.0001832502,0.000631943,0.000329604,0.000317954],"domain_scores_gemma":[0.9919866,0.005954087,0.0005776554,0.0005259181,0.0007076138,0.00024808],"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":[0.001590017,0.0004971818,0.06447483,0.000380509,0.00114223,0.001062002,0.0003417421,0.6304415,0.006475973,0.005933304,0.01294661,0.2747141],"study_design_scores_gemma":[0.000101033,0.00007893189,0.002013732,0.00004693252,0.00008337481,0.0001243766,0.00003096942,0.9846156,0.0008322923,0.01118174,0.0008656579,0.00002539092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.162728,0.002597099,0.8259892,0.0009859296,0.0003004823,0.0004200729,0.002252319,0.003413456,0.001313438],"genre_scores_gemma":[0.7163137,0.0007188342,0.2749118,0.0006702292,0.0003638726,0.0004577977,0.005271549,0.0002676479,0.001024706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009037538,"threshold_uncertainty_score":0.04779565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04321779750810731,"score_gpt":0.3150316297146084,"score_spread":0.271813832206501,"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."}}