{"id":"W2979945966","doi":"10.1109/tcbb.2019.2945761","title":"EPGA-SC : A Framework for<i>de novo</i>Assembly of Single-Cell Sequencing Reads","year":2019,"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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Contig; Sequence assembly; Hybrid genome assembly; Computer science; DNA sequencing; Single cell sequencing; Deep sequencing; Computational biology; Nanopore sequencing; Genome; Algorithm; Biology; Genetics; Exome sequencing; Mutation; Gene","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":[0.0001421281,0.0001459843,0.0001824787,0.00006569339,0.0001092799,0.00001117484,0.0001303098,0.000209157,0.00000572766],"category_scores_gemma":[0.00002482316,0.0001347994,0.00009231586,0.00007093077,0.00009941409,0.000002620758,0.00001043433,0.0000909363,0.000005047252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001831124,"about_ca_system_score_gemma":0.00009902463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004028662,"about_ca_topic_score_gemma":0.000003955087,"domain_scores_codex":[0.9992528,0.00002374365,0.0003064878,0.0001684854,0.00005725168,0.0001912209],"domain_scores_gemma":[0.9992923,0.0002201941,0.0001297486,0.0001890152,0.0001215375,0.00004727725],"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":[0.0004455889,0.000286762,0.002751277,0.0003638625,0.0003925641,5.351911e-7,0.001217051,0.07646388,0.8976368,0.00313568,0.0001021928,0.01720378],"study_design_scores_gemma":[0.004991617,0.008718086,0.002825729,0.0002164409,0.0002733192,0.0001753908,0.001764382,0.04925075,0.8529804,0.07066292,0.006662355,0.001478591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5618391,0.000108088,0.4372806,0.0001058683,0.000172154,0.0001706898,0.0001448688,0.00000404618,0.0001746098],"genre_scores_gemma":[0.8229083,0.00009102884,0.1765125,0.0003156972,0.00003933258,0.00001330855,0.00005659303,0.000008890395,0.00005431826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2610692,"threshold_uncertainty_score":0.5496959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01982537621100055,"score_gpt":0.2612341450046684,"score_spread":0.2414087687936678,"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."}}