{"id":"W3047407562","doi":"10.1186/s12859-020-03697-x","title":"GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis","year":2020,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; University of Toronto; Ontario Institute for Cancer Research","funders":"University of Peradeniya; Government of Ontario; Government of Canada; Ontario Institute for Cancer Research; Genome Canada; Ontario Genomics; Innopolis University; University of New South Wales; Nvidia","keywords":"Computer science; Parallel computing; Overhead (engineering); Nanopore sequencing; Software; General-purpose computing on graphics processing units; Computational science; DNA sequencing; Graphics; DNA","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001164264,0.0002199647,0.0003432615,0.0000440285,0.0001314136,0.00003422283,0.0001901667,0.00009980241,0.00003164288],"category_scores_gemma":[0.00001917885,0.0001908664,0.000265024,0.0002209243,0.0000615088,0.000002111126,0.0001242188,0.00004728881,0.00001117061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001663368,"about_ca_system_score_gemma":0.00009199719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003808645,"about_ca_topic_score_gemma":0.00002182934,"domain_scores_codex":[0.9989228,0.00002933424,0.0004314678,0.000219975,0.0001388974,0.0002575423],"domain_scores_gemma":[0.9992842,0.00002724499,0.0002042224,0.0001889634,0.0001631343,0.0001322135],"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":[0.006122676,0.0009812766,0.01751832,0.0009730896,0.02908679,0.000004954004,0.0378777,0.2689594,0.5368012,0.002472599,0.08505375,0.01414822],"study_design_scores_gemma":[0.003382815,0.002917829,0.004560674,0.00001412573,0.001226184,0.00000291292,0.007757191,0.6984217,0.234018,0.0001263645,0.04659488,0.0009772889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3908364,0.001390389,0.6027712,0.0003628377,0.0001600011,0.001689298,0.0007911671,0.00002229705,0.001976417],"genre_scores_gemma":[0.9528089,0.00009679054,0.04579145,0.0006281669,0.0001223863,0.00006936936,0.000403117,0.00001165934,0.00006818277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5619724,"threshold_uncertainty_score":0.7783307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07105467821933206,"score_gpt":0.2832542641391247,"score_spread":0.2121995859197926,"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."}}