{"id":"W2945501761","doi":"10.1093/bioinformatics/btz413","title":"HyAsP, a greedy tool for plasmids identification","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contig; Plasmid; Computer science; Genome; Greedy algorithm; Identification (biology); Computational biology; Biology; Genetics; Gene; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004297239,0.003903612,0.002299457,0.003587267,0.002262556,0.003475921,0.003657677,0.001976127,0.01752511],"category_scores_gemma":[0.0160443,0.002337936,0.002619919,0.003644705,0.001153896,0.003911945,0.004623594,0.002761517,0.02187783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108339,"about_ca_system_score_gemma":0.002633278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537794,"about_ca_topic_score_gemma":0.00314847,"domain_scores_codex":[0.9970009,0.0007184117,0.0003027522,0.0008974427,0.0008545453,0.0002260363],"domain_scores_gemma":[0.9942396,0.003493025,0.0005135125,0.0009453389,0.0005221593,0.0002863564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003322564,0.0006200323,0.0205113,0.006387223,0.001324058,0.001398781,0.001261597,0.01581768,0.07696138,0.01299347,0.488827,0.370575],"study_design_scores_gemma":[0.001906673,0.000931761,0.01536306,0.0008668204,0.0006979746,0.004577113,0.0008575661,0.3551785,0.1204122,0.06371233,0.4347345,0.000761462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01509468,0.001597879,0.5189216,0.0005391511,0.0003471006,0.0005130603,0.03158915,0.4278027,0.003594737],"genre_scores_gemma":[0.04302298,0.0006286583,0.8521836,0.000650949,0.0001275867,0.001007736,0.06466249,0.03529017,0.002425746],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01752511,"threshold_uncertainty_score":0.05862731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003540163125125,"score_gpt":0.2286035975719128,"score_spread":0.2185681959406616,"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."}}