{"id":"W3173187119","doi":"10.1186/s12859-021-04267-5","title":"Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing","year":2021,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University Abu Dhabi; New York University","keywords":"Barcode; Computer science; Decoding methods; Probabilistic logic; Benchmark (surveying); Data mining; Algorithm; Artificial intelligence","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":[0.008935501,0.002785753,0.002216076,0.002511148,0.001118974,0.003497271,0.004811261,0.002472071,0.01658455],"category_scores_gemma":[0.03032473,0.002284433,0.001899346,0.00141816,0.001822774,0.002573065,0.003476095,0.004472625,0.00948359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001944009,"about_ca_system_score_gemma":0.005096795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004396586,"about_ca_topic_score_gemma":0.005291025,"domain_scores_codex":[0.9956889,0.001010584,0.0003371862,0.0008351194,0.001897839,0.0002303844],"domain_scores_gemma":[0.9896955,0.006050094,0.001149698,0.001010083,0.001681354,0.0004131468],"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":[0.002245428,0.0004553084,0.02353966,0.002757724,0.001147807,0.001038626,0.00132783,0.2447567,0.09259439,0.06819089,0.1469629,0.4149827],"study_design_scores_gemma":[0.0001953492,0.0001219817,0.001572869,0.000124089,0.00007130647,0.0002932139,0.00005182517,0.8869225,0.05665662,0.0304147,0.02333318,0.0002424106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005089582,0.0001741838,0.9079888,0.0002060131,0.00008521673,0.0001086344,0.002638905,0.08255499,0.001153758],"genre_scores_gemma":[0.07372917,0.0002812737,0.8968164,0.0006042219,0.0001056721,0.0008505416,0.007898635,0.01715684,0.002557217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01658455,"threshold_uncertainty_score":0.05548084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.016237025944968,"score_gpt":0.251207369551136,"score_spread":0.234970343606168,"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."}}