{"id":"W3026606344","doi":"10.1002/advs.201903562","title":"CRISPR‐Net: A Recurrent Convolutional Network Quantifies CRISPR Off‐Target Activities with Mismatches and Indels","year":2020,"lang":"en","type":"article","venue":"Advanced Science","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Health and Medical Research Fund; City University of Hong Kong","keywords":"CRISPR; Computer science; Indel; Computational biology; In silico; Guide RNA; Code (set theory); Tree (set theory); Artificial intelligence; Data mining; Gene; Cas9; Genetics; Biology; Set (abstract data type)","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":[0.0004679147,0.001274961,0.0005780471,0.0008540392,0.0002650407,0.0006072856,0.001200611,0.0007299978,0.001991387],"category_scores_gemma":[0.001298989,0.0003333868,0.0005377819,0.0006506404,0.000317695,0.0007371613,0.000606996,0.0008637462,0.0006659339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100506,"about_ca_system_score_gemma":0.0009766044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01043933,"about_ca_topic_score_gemma":0.01516016,"domain_scores_codex":[0.9997442,0.00002376916,0.00001248812,0.0001064278,0.00007246505,0.00004058855],"domain_scores_gemma":[0.9996456,0.0001321547,0.0000585536,0.00004734698,0.0000857985,0.00003054899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005410577,0.0002612379,0.009673618,0.0003532509,0.0003789578,0.0002500055,0.00005126847,0.6836798,0.06932818,0.005226736,0.02094688,0.209309],"study_design_scores_gemma":[0.000008995135,0.00004836898,0.001214339,0.00000761877,0.00002957433,0.0000330274,0.000005543328,0.9820521,0.01377689,0.001551662,0.001257227,0.0000146698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3209177,0.002390793,0.6231218,0.0007929208,0.0003558272,0.0001506697,0.01163985,0.03180265,0.00882789],"genre_scores_gemma":[0.8055099,0.0007654743,0.1641168,0.0003880684,0.0000568136,0.0001878214,0.01869202,0.0006949052,0.009588114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01043933,"threshold_uncertainty_score":0.02075714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027752449166285,"score_gpt":0.2838149168541789,"score_spread":0.2735373923625161,"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."}}