{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105005,0.0006572455,0.000769087,0.001500568,0.0002464226,0.0007516347,0.0004303189,0.0006455533,0.0004161617],"category_scores_gemma":[0.001933359,0.0001948063,0.0007239566,0.001604568,0.0004768205,0.0006616104,0.0003166687,0.0008049188,0.0002334248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006060338,"about_ca_system_score_gemma":0.0002281092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00257354,"about_ca_topic_score_gemma":0.00266114,"domain_scores_codex":[0.9995869,0.0001479446,0.00002194883,0.0001527625,0.00005838026,0.0000321307],"domain_scores_gemma":[0.9988023,0.0008625314,0.0001598427,0.00008397293,0.00006198073,0.00002938801],"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":[0.0003878829,0.0006077334,0.283186,0.0002378592,0.0007953871,0.0002351373,0.0002406498,0.3718998,0.107717,0.002607173,0.0005947859,0.2314906],"study_design_scores_gemma":[0.000008811773,0.00009608201,0.09680212,0.00001215941,0.00004646302,0.00006182725,0.00005611452,0.8884602,0.00828483,0.005511158,0.0006258804,0.0000344598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7767775,0.001158186,0.2190203,0.0003953859,0.00002667418,0.00003859114,0.001098545,0.0005552129,0.0009297039],"genre_scores_gemma":[0.9517403,0.0003806543,0.0461117,0.00009286741,0.0000335954,0.00005302053,0.00103115,0.00003081637,0.0005257264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00257354,"threshold_uncertainty_score":0.005553305,"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."}}