{"id":"W3090576692","doi":"10.1002/ctm2.194","title":"Improving transgene expression and CRISPR‐Cas9 efficiency with molecular engineering‐based molecules","year":2020,"lang":"en","type":"article","venue":"Clinical and Translational Medicine","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"National Key Research and Development Program of China; Natural Science Foundation of Guangdong Province; Government of Jiangxi Province; National Natural Science Foundation of China","keywords":"CRISPR; Cas9; Transgene; Genome editing; Biology; Gene; Computational biology; Genetic enhancement; Plasmid; Cell biology; Genetics","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":[0.000119608,0.0001428245,0.0001775754,0.00002145085,0.00003493932,0.000006585394,0.00005627599,0.00009106796,0.000009769074],"category_scores_gemma":[0.00007400888,0.0001069584,0.00003764452,0.00006338448,0.0001423557,0.000002898466,0.00001172235,0.0001131658,3.533291e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":5.004624e-7,"about_ca_system_score_gemma":0.00002468606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002322641,"about_ca_topic_score_gemma":9.420589e-7,"domain_scores_codex":[0.99913,0.00001957366,0.000249825,0.0003351979,0.0001354752,0.0001299061],"domain_scores_gemma":[0.9995906,0.0000489893,0.00002742623,0.00008894555,0.00003476671,0.0002093174],"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":[0.0002424383,0.00003837378,0.004119588,0.0001689172,0.00003403339,0.00001578217,0.00008724323,0.005850131,0.9816875,0.0001284165,0.00002818192,0.007599366],"study_design_scores_gemma":[0.01771686,0.006537686,0.06141991,0.0005218958,0.0003942729,0.00007302916,0.0001275266,0.2395993,0.6600791,0.0001224098,0.01210767,0.001300377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4186034,0.002928104,0.5761815,0.002080968,0.00003368867,0.0001092173,0.000006418336,0.0000150737,0.00004164783],"genre_scores_gemma":[0.9910181,0.0001039066,0.007918269,0.0006729598,0.0002114751,0.000006785201,0.00004727108,0.00001733283,0.000003879792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5724148,"threshold_uncertainty_score":0.4361636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574441857816584,"score_gpt":0.3119143305036433,"score_spread":0.2961699119254775,"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."}}