{"id":"W3203740205","doi":"10.1038/s41598-021-98934-5","title":"Genomic background selection to reduce the mutation load after random mutagenesis","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Christian-Albrechts-Universität zu Kiel; Chinesisch-Deutsche Zentrum für Wissenschaftsförderung; Deutsche Forschungsgemeinschaft; University of Alberta","keywords":"Mutagenesis; Selection (genetic algorithm); Mutation; Genetics; Computational biology; Computer science; Biology; Gene; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005506399,0.00009544135,0.0000775123,0.00002990823,0.0001606691,0.0001666777,0.00006378393,0.00005358441,0.00007957855],"category_scores_gemma":[0.00009790344,0.0000801369,0.00007809981,0.0002495422,0.00004033836,0.000003487742,0.00007003815,0.00004064841,0.00002231707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003188973,"about_ca_system_score_gemma":0.0001909255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001176565,"about_ca_topic_score_gemma":0.000127834,"domain_scores_codex":[0.9988527,0.00003572762,0.0002141385,0.0005146159,0.0001978557,0.000184922],"domain_scores_gemma":[0.999198,0.000006319458,0.00004898247,0.0004757917,0.0002037011,0.00006721372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002832374,0.00001330948,0.0004709191,0.00000683214,0.00001845592,0.00006679078,0.0001169527,0.002996141,0.9894661,0.000001062775,0.003720065,0.003095021],"study_design_scores_gemma":[0.0001473129,0.00001920314,0.008758151,0.00000618887,0.00002085441,0.0005516781,0.0001069312,0.00008333018,0.8858967,0.00006854701,0.1042194,0.0001217388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824686,0.0009976887,0.01323927,0.0001438021,0.002572395,0.0001767781,0.000001352793,0.00001258789,0.0003874979],"genre_scores_gemma":[0.9905382,0.00001005656,0.0007992154,0.00008528363,0.0002149995,0.00005049462,0.00004759501,0.00001415306,0.008239934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1035695,"threshold_uncertainty_score":0.3267888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007735789272984202,"score_gpt":0.2767048886155344,"score_spread":0.2689690993425502,"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."}}