{"id":"W4405868773","doi":"10.1101/2024.12.27.630546","title":"Chemically-inducible CRISPR/Cas9 circuits for ultra-high dynamic range gene perturbation","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Genentech","keywords":"CRISPR; Electronic circuit; Dynamic range; Genome editing; Computational biology; Gene; Perturbation (astronomy); Genetics; Biology; Physics; Computer science; Quantum mechanics; Optics","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":[0.0003140982,0.0004248575,0.0003103731,0.0003738876,0.0002408398,0.0005900349,0.0008900876,0.0003858713,0.001852257],"category_scores_gemma":[0.0002452149,0.00024526,0.0002870943,0.0002107562,0.000466488,0.0004003622,0.0006354764,0.0009655167,0.0008404516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006093943,"about_ca_system_score_gemma":0.000316991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004822101,"about_ca_topic_score_gemma":0.0009513072,"domain_scores_codex":[0.9995903,0.00003324719,0.00003874082,0.0001213641,0.0001733057,0.00004306652],"domain_scores_gemma":[0.9997619,0.00003540326,0.00008035808,0.00004193338,0.00003460321,0.00004591241],"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":[0.00001652662,0.00001302726,0.00004669419,0.00003591727,0.000003857955,0.00002307806,0.000007240491,0.0002808136,0.9955598,0.0008165516,0.0002180526,0.002978405],"study_design_scores_gemma":[0.000006315743,0.00003823025,0.00027465,0.000003651871,0.000007313168,0.00009128876,0.000003993749,0.002598116,0.9892553,0.0001581182,0.007554861,0.000008066572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.421359,0.001853655,0.5556716,0.0005587906,0.0003856098,0.0005384959,0.002341003,0.006670831,0.01062103],"genre_scores_gemma":[0.807317,0.0009978282,0.1747806,0.0003086066,0.00005395304,0.0005195694,0.001944194,0.0006741831,0.01340399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001852257,"threshold_uncertainty_score":0.00619638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008698597850455753,"score_gpt":0.2516028252856609,"score_spread":0.2429042274352052,"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."}}