{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001176587,0.0001418626,0.0001240403,0.00002223877,0.0001819805,0.00004250046,0.0001759566,0.00003931336,0.000007202568],"category_scores_gemma":[0.00008080754,0.0001245024,0.00002367058,0.0002025532,0.0004141115,0.00002120888,0.000116679,0.0000849275,0.000001532629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008515965,"about_ca_system_score_gemma":0.0001247177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002367973,"about_ca_topic_score_gemma":0.00001531537,"domain_scores_codex":[0.9989298,0.00001268038,0.0001182042,0.0004287325,0.0001965557,0.0003140472],"domain_scores_gemma":[0.9995701,0.00002168236,0.00004410099,0.0001569522,0.00006060606,0.0001465925],"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":[0.0002844649,0.00003445074,0.006461822,0.00008133673,0.00003392491,0.00000577368,0.0008227037,0.06956607,0.9129845,0.0007361781,0.001141993,0.007846755],"study_design_scores_gemma":[0.001089254,0.001274912,0.01382635,0.00009759508,0.00002340534,0.00004376196,0.001475343,0.01149003,0.9285341,0.0003840162,0.04100625,0.0007549651],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8989009,0.005934321,0.09397749,0.0005371905,0.0001764833,0.0001595295,0.00001464522,0.00002950815,0.0002699018],"genre_scores_gemma":[0.9865679,0.0003257685,0.01256942,0.0003118124,0.0001380423,0.0000148941,0.000009209754,0.0000110159,0.00005189322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.087667,"threshold_uncertainty_score":0.5077062,"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."}}