{"id":"W4389239981","doi":"10.1158/2326-6074.tumimm23-a045","title":"Abstract A045: Development of automated deep learning-based off-target distribution prediction system for CRISPR-Cas13 system","year":2023,"lang":"en","type":"article","venue":"Cancer Immunology Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CRISPR; Genome editing; Computational biology; Computer science; RNA; Gene; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009750856,0.0001390358,0.0002047814,0.0001489325,0.0002549923,0.00001506027,0.00021165,0.0002716316,0.000007450904],"category_scores_gemma":[0.0001482005,0.0001420926,0.0000749152,0.000330275,0.0001006157,0.00000347603,0.00009401207,0.0002100866,0.00001623389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002337974,"about_ca_system_score_gemma":0.0003238144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002623484,"about_ca_topic_score_gemma":0.00001655828,"domain_scores_codex":[0.9984654,0.00009921414,0.0003693599,0.0003486771,0.000213128,0.0005042764],"domain_scores_gemma":[0.9990658,0.00006532305,0.00008294649,0.0002654162,0.0004650231,0.00005550459],"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":[0.0003889926,0.00003677558,0.001694797,0.001287578,0.000270894,0.000003956629,0.0001411059,0.03399354,0.9538845,0.00003235134,0.00178332,0.006482126],"study_design_scores_gemma":[0.001014446,0.0002769084,0.03293056,0.0002021636,0.00001554268,0.00000550644,0.001161447,0.0681689,0.874279,0.00000109522,0.02178284,0.0001615941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535937,0.005080734,0.03923604,0.00005994047,0.0006318295,0.0007236046,0.0002194102,0.0003612012,0.00009352048],"genre_scores_gemma":[0.9972868,0.0001199956,0.0002484588,0.000001509581,0.0001095451,0.0006218507,0.001453771,0.00003116509,0.0001269275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07960556,"threshold_uncertainty_score":0.579437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02728108774433331,"score_gpt":0.3724679905078316,"score_spread":0.3451869027634983,"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."}}