{"id":"W2897603144","doi":"10.1109/tcbb.2018.2876855","title":"MEC: Misassembly Error Correction in Contigs based on Distribution of Paired-End Reads and Statistics of GC-contents","year":2018,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Contig; Sequence assembly; Computer science; DNA sequencing; Genome; Computational biology; Data mining; Biology; Genetics; DNA","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":[0.003866817,0.00218961,0.001417346,0.004889918,0.001029973,0.001415749,0.002039713,0.001108559,0.001645175],"category_scores_gemma":[0.01876488,0.0005238211,0.001123282,0.003458767,0.0008388302,0.00211895,0.001692912,0.001038188,0.001274361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008963699,"about_ca_system_score_gemma":0.00186601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00529176,"about_ca_topic_score_gemma":0.008792484,"domain_scores_codex":[0.9963637,0.0004683208,0.0004171895,0.00114609,0.001400327,0.0002044261],"domain_scores_gemma":[0.9883319,0.003612419,0.001891248,0.002773314,0.003125927,0.0002652293],"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":[0.001180038,0.0002575256,0.07007764,0.001808951,0.0009370294,0.0006339472,0.00075023,0.05275857,0.1004804,0.004705344,0.01783728,0.7485731],"study_design_scores_gemma":[0.0001652616,0.0007879655,0.07296871,0.0002482518,0.0004657328,0.002474859,0.0004741253,0.631429,0.2484349,0.009031769,0.03307999,0.0004395367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1150278,0.002980719,0.8230863,0.0002595889,0.0002947793,0.0004002583,0.004755709,0.0503845,0.002810283],"genre_scores_gemma":[0.3008537,0.000688152,0.680931,0.000252584,0.0001148418,0.0003496558,0.01204991,0.002356259,0.002403869],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00529176,"threshold_uncertainty_score":0.02044994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660439633401863,"score_gpt":0.266012007464778,"score_spread":0.2494076111307593,"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."}}