{"id":"W2132840768","doi":"10.1093/bioinformatics/btu719","title":"deML: robust demultiplexing of Illumina sequences using a likelihood-based approach","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":215,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Max-Planck-Gesellschaft","keywords":"Pooling; Quality Score; Multiplexing; Computer science; Sample (material); Set (abstract data type); Illumina dye sequencing; Index (typography); Software; DNA sequencing; Sequence (biology); Data mining; Computational biology; Algorithm; Biology; Genetics; Artificial intelligence; DNA; Metric (unit); Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00753489,0.002406037,0.001553265,0.00298255,0.001097163,0.002420032,0.003137659,0.001487147,0.01975896],"category_scores_gemma":[0.01625907,0.002001446,0.002509654,0.002440016,0.001164385,0.002147014,0.003005823,0.003603064,0.01371593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001759239,"about_ca_system_score_gemma":0.001461517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520884,"about_ca_topic_score_gemma":0.002800419,"domain_scores_codex":[0.9968172,0.00111008,0.0002032766,0.0008365283,0.0008756527,0.0001572505],"domain_scores_gemma":[0.9957775,0.002119119,0.0004278798,0.0009047458,0.0006413043,0.0001294581],"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":[0.001426318,0.0003001292,0.005236229,0.002275596,0.0009152804,0.0005484423,0.0005702131,0.06660495,0.07855006,0.02917906,0.1383112,0.6760825],"study_design_scores_gemma":[0.0003515447,0.0002188422,0.003045271,0.0002158987,0.0001318956,0.0007088167,0.00009804911,0.6688353,0.1593741,0.0599673,0.1067616,0.0002914558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003895831,0.0003427232,0.9562114,0.0002608218,0.0000977005,0.0002046743,0.003642984,0.03336566,0.001978291],"genre_scores_gemma":[0.01849049,0.0001718784,0.9657068,0.0003224094,0.00006369881,0.0005470396,0.007570422,0.004811271,0.002316065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01975896,"threshold_uncertainty_score":0.06610036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02748290182311156,"score_gpt":0.2342808467574251,"score_spread":0.2067979449343136,"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."}}