{"id":"W4391846268","doi":"10.1101/2024.02.14.580307","title":"Predicting fitness related traits using gene expression and machine learning","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Swedish Collegium for Advanced Study","keywords":"Biology; Natural selection; Gene; Phenotype; Genomics; Selection (genetic algorithm); Machine learning; Computational biology; Genetics; Artificial intelligence; Evolutionary biology; Computer science; Genome","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":[0.0008324854,0.0005787136,0.000604714,0.001299999,0.0001804943,0.0007071943,0.0002834547,0.0006098563,0.0006256328],"category_scores_gemma":[0.001321794,0.0001581725,0.0005087681,0.001175809,0.0003841978,0.0004395359,0.0002040644,0.0006014775,0.0002717403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005031269,"about_ca_system_score_gemma":0.0002110116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343865,"about_ca_topic_score_gemma":0.001531217,"domain_scores_codex":[0.999688,0.000117384,0.00001540281,0.0001068172,0.00004735703,0.00002510488],"domain_scores_gemma":[0.9990569,0.0006599908,0.0001259176,0.00005111561,0.00007638775,0.0000298378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000649048,0.0008807557,0.3391687,0.0002745033,0.0006422077,0.0002186191,0.0001534062,0.2792625,0.2127851,0.001217197,0.0009206025,0.1638273],"study_design_scores_gemma":[0.00001026593,0.00011764,0.1302642,0.00001128857,0.00004040388,0.00004663381,0.00004883734,0.8483443,0.01779459,0.002763257,0.0005307242,0.00002784327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9132637,0.0006539726,0.0828593,0.0003655344,0.00002689185,0.00003388443,0.001348484,0.0005369386,0.000911213],"genre_scores_gemma":[0.968872,0.0001668603,0.02903561,0.00005902887,0.00002818056,0.00003148347,0.001200134,0.00002452323,0.0005821597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002343865,"threshold_uncertainty_score":0.004660487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190671290036064,"score_gpt":0.2112758581495587,"score_spread":0.1922087291459523,"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."}}