{"id":"W2131813051","doi":"10.1093/bioinformatics/btu762","title":"EPGA: <i>de novo</i> assembly using the distributions of reads and insert size","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Contig; Sequence assembly; Hybrid genome assembly; De Bruijn graph; Computer science; Sequence (biology); Insert (composites); Genome; De Bruijn sequence; Graph; Extension (predicate logic); Reference genome; Algorithm; Computational biology; Biology; Theoretical computer science; Genetics; Mathematics; Combinatorics","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":[0.000262833,0.00008130359,0.00008696079,0.00001250534,0.00007717452,0.00002073702,0.0001211051,0.00006820361,0.000001588814],"category_scores_gemma":[0.0002044556,0.00006017957,0.0000362048,0.00006692921,0.00008356795,0.000004840298,0.00009817728,0.00004390469,0.000002075774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009315894,"about_ca_system_score_gemma":0.00003804818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000799857,"about_ca_topic_score_gemma":0.000003654588,"domain_scores_codex":[0.9994871,0.00002231152,0.0001907296,0.00006185513,0.00008494435,0.0001530612],"domain_scores_gemma":[0.9995275,0.00003418355,0.00009756636,0.0002417889,0.00005708544,0.00004190996],"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":[0.00002443501,0.0001159683,0.01429744,0.0003065209,0.0001811987,6.679958e-7,0.001065642,0.0002067345,0.9678826,0.002358935,0.003104952,0.01045493],"study_design_scores_gemma":[0.002968941,0.00103144,0.03883553,0.0001170591,0.0003282804,0.0002771586,0.001950388,0.08319799,0.6194039,0.0010086,0.2500797,0.0008010657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9385754,0.0001254518,0.05938879,0.0000944111,0.00005198189,0.0001218367,0.00003862971,0.000006741197,0.001596746],"genre_scores_gemma":[0.9806467,0.0001079558,0.01888164,0.0001422517,0.00006959066,0.000003834654,0.00004121633,0.00000791077,0.00009884335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3484787,"threshold_uncertainty_score":0.2454052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103939165250195,"score_gpt":0.2415699575942003,"score_spread":0.2305305659416983,"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."}}