{"id":"W4407862063","doi":"10.1093/gbe/evae275","title":"Predicting Fitness-Related Traits Using Gene Expression and Machine Learning","year":2024,"lang":"en","type":"article","venue":"Genome Biology and Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biology; Natural selection; Gene; Genomics; Phenotype; Selection (genetic algorithm); Genetics; Evolutionary biology; Genetic Fitness; Computational biology; Phenotypic trait; Coevolution; Machine learning; Genome; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001093168,0.00009081341,0.00007611782,0.00003987526,0.0002033349,0.00001662205,0.00003319509,0.0002236495,0.00001924491],"category_scores_gemma":[0.00001818021,0.00008309132,0.00002312204,0.00005456806,0.00007433813,0.000005486495,0.00007935434,0.0001039194,0.000001904353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008433077,"about_ca_system_score_gemma":0.00001669473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002233941,"about_ca_topic_score_gemma":0.000005110339,"domain_scores_codex":[0.9994169,0.00006203514,0.0001024035,0.000263393,0.00002835303,0.0001269362],"domain_scores_gemma":[0.9998546,0.000006309091,0.00002981722,0.0000479154,0.00001797952,0.00004338016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002013018,0.000002513811,0.09472439,0.00002041083,0.00002585986,0.000001046034,0.0001472255,0.0005507184,0.9032878,0.0001447613,0.000004797072,0.001070393],"study_design_scores_gemma":[0.001593526,0.0007251379,0.8921094,0.000109667,0.0002094996,0.0006209656,0.0004730469,0.03119083,0.04449985,0.00341789,0.02420785,0.000842317],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770328,0.01718141,0.00540403,0.00002854344,0.0001662688,0.00006422718,0.00003151967,0.00002413023,0.00006703824],"genre_scores_gemma":[0.9983781,0.0003074,0.0007269215,0.00001533168,0.0001228159,0.000001177952,0.0002437257,0.000006782336,0.0001977635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8587879,"threshold_uncertainty_score":0.3388366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257152251941745,"score_gpt":0.233926810677991,"score_spread":0.2213552881585735,"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."}}