{"id":"W2607286649","doi":"10.1089/cmb.2017.0021","title":"Zseq: An Approach for Preprocessing Next-Generation Sequencing Data","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preprocessor; Sequence (biology); DNA sequencing; Sequence assembly; Computer science; Computational biology; Genome; Biology; Discriminative model; Genomics; Algorithm; Genetics; Artificial intelligence; DNA; Gene","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":[0.002412484,0.001911104,0.001504194,0.003384996,0.001709762,0.002468293,0.001971535,0.001017689,0.009948754],"category_scores_gemma":[0.003976141,0.001407991,0.001825456,0.002956498,0.0004870516,0.001288993,0.001643727,0.002663008,0.005381931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224075,"about_ca_system_score_gemma":0.002026428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003136826,"about_ca_topic_score_gemma":0.0047012,"domain_scores_codex":[0.9981481,0.0002652822,0.0001884503,0.0005985878,0.0006649089,0.0001347065],"domain_scores_gemma":[0.9988387,0.0004468923,0.0001486505,0.0001552028,0.0003575805,0.00005304078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00138882,0.0002330426,0.008020926,0.003504061,0.001181617,0.0007947023,0.001118347,0.01502611,0.4876655,0.01395295,0.06725066,0.3998632],"study_design_scores_gemma":[0.0002146986,0.0004034127,0.01488308,0.0002569715,0.000489124,0.001192921,0.0003831995,0.1124124,0.464564,0.0234369,0.3812263,0.0005369549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01424674,0.001598553,0.9238418,0.0002238286,0.0003375288,0.0005239295,0.02367694,0.03251685,0.00303388],"genre_scores_gemma":[0.02420202,0.001059267,0.9276916,0.0004116258,0.00009325363,0.001424694,0.03707463,0.00450124,0.003541672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009948754,"threshold_uncertainty_score":0.03328192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161899380631361,"score_gpt":0.3635272799106088,"score_spread":0.1473373418474727,"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."}}