{"id":"W3147137656","doi":"10.3390/molecules26072053","title":"Synergizing Off-Target Predictions for In Silico Insights of CENH3 Knockout in Cannabis through CRISPR/Cas","year":2021,"lang":"en","type":"article","venue":"Molecules","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Guelph","funders":"","keywords":"CRISPR; In silico; Support vector machine; Receiver operating characteristic; Random forest; Computer science; Computational biology; Classifier (UML); Machine learning; Artificial intelligence; Biology; Genetics; Gene","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":[0.00004488027,0.0001264672,0.0001735829,0.00006081413,0.00002585992,0.000008747569,0.00009626712,0.0001236128,0.000005266284],"category_scores_gemma":[0.0001360903,0.0001387705,0.00008366934,0.0001688347,0.00002937668,0.000004062728,0.00006967149,0.00007194753,5.236266e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001916542,"about_ca_system_score_gemma":0.00009173243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001040756,"about_ca_topic_score_gemma":0.001149419,"domain_scores_codex":[0.9991385,0.00002919249,0.0002596584,0.0002867212,0.00007686657,0.0002090591],"domain_scores_gemma":[0.9995984,0.00001231138,0.00003636779,0.0002403942,0.00007724247,0.0000352382],"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":[0.00003137235,0.0001249522,0.001334026,0.0000954818,0.00003550465,0.00001733559,0.0004627639,0.03297007,0.9625329,0.0006299145,0.001090101,0.000675513],"study_design_scores_gemma":[0.0005984925,0.00008855801,0.006544634,0.00004901773,0.00001085796,0.00000590182,0.0002775859,0.002188746,0.9651054,0.0003974204,0.02457267,0.0001607354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.856172,0.009936808,0.1321271,0.0002631601,0.0002014049,0.000228692,0.00004241609,0.00001148155,0.001016954],"genre_scores_gemma":[0.993591,0.0002660991,0.005604453,0.00008240078,0.00007105621,0.00006961764,0.0001006719,0.00002297779,0.0001917822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.137419,"threshold_uncertainty_score":0.5658898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008474096416350413,"score_gpt":0.2903165728239382,"score_spread":0.2818424764075877,"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."}}