{"id":"W4375955770","doi":"10.1093/bioinformatics/btad309","title":"Genome mining for anti-CRISPR operons using machine learning","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases; University of Nebraska-Lincoln; University of Toronto; U.S. Department of Agriculture","keywords":"CRISPR; Genome; Operon; Computational biology; Context (archaeology); Computer science; Python (programming language); Gene; Biology; Genetics; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001478351,0.001475694,0.001387012,0.003932244,0.0007073882,0.00118716,0.001850581,0.001316096,0.001905455],"category_scores_gemma":[0.005646828,0.0006051589,0.001919164,0.002325904,0.00041587,0.0008532868,0.000937642,0.001241362,0.001263364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007765738,"about_ca_system_score_gemma":0.001296531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002728114,"about_ca_topic_score_gemma":0.002849872,"domain_scores_codex":[0.9988285,0.0001853966,0.0001233831,0.0004882239,0.0002719118,0.0001025926],"domain_scores_gemma":[0.9969977,0.001800262,0.0004738719,0.0001858607,0.0004399027,0.0001024663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001326993,0.001326117,0.1076864,0.004069544,0.0009419658,0.00180442,0.0004876042,0.2108199,0.08896776,0.004855887,0.03644071,0.5412726],"study_design_scores_gemma":[0.00008854276,0.0002178895,0.007629079,0.0001416661,0.0001763888,0.0005420463,0.0001447988,0.9365047,0.03680859,0.008148106,0.009545675,0.0000524597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3276218,0.004069872,0.5794847,0.001637213,0.0001550422,0.0005220044,0.02750424,0.05446346,0.004541609],"genre_scores_gemma":[0.4249014,0.0006571159,0.5305088,0.000810329,0.00006042097,0.0004528998,0.04064668,0.0008979295,0.001064388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003932244,"threshold_uncertainty_score":0.007818341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191280019472707,"score_gpt":0.3098302355894656,"score_spread":0.2879174353947385,"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."}}