{"id":"W4413110219","doi":"10.1186/s13015-025-00281-x","title":"b-move: faster lossless approximate pattern matching in a run-length compressed index","year":2025,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institutes of Health; National Human Genome Research Institute; Vlaamse regering; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek","keywords":"Computer science; Search engine indexing; Lossless compression; Index (typography); RefSeq; Memory footprint; Pattern matching; Scalability; Matching (statistics); Overhead (engineering); Theoretical computer science; Data mining; Genome; Algorithm; Data compression; Information retrieval; Mathematics; Artificial intelligence; Database","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":[0.0006230518,0.0009995074,0.001153042,0.001616199,0.0006465054,0.001681266,0.002569399,0.0008374893,0.006441416],"category_scores_gemma":[0.003855894,0.0004864694,0.0007157632,0.003522665,0.0005898811,0.004183003,0.00264367,0.001095511,0.005458747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000778043,"about_ca_system_score_gemma":0.001658142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004679794,"about_ca_topic_score_gemma":0.00520448,"domain_scores_codex":[0.9986367,0.0001160245,0.0001153817,0.0002539294,0.0007476835,0.0001303302],"domain_scores_gemma":[0.9988658,0.0002485649,0.0001163543,0.0004460156,0.0002399875,0.00008332775],"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":[0.001914829,0.0004930982,0.003235339,0.0006676341,0.0001517847,0.00054729,0.0005701106,0.0204404,0.1051581,0.02718896,0.06370567,0.7759268],"study_design_scores_gemma":[0.0006373611,0.001090342,0.002240548,0.0001262228,0.0001429225,0.001701285,0.0004779919,0.7094913,0.1484904,0.03019627,0.1051889,0.0002165073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08655799,0.003497995,0.8442172,0.0005339556,0.0005471045,0.0004204187,0.003001921,0.04745845,0.01376491],"genre_scores_gemma":[0.2493844,0.0009340763,0.7227748,0.0005742605,0.0001918885,0.0005558466,0.01218316,0.002046409,0.01135514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006441416,"threshold_uncertainty_score":0.02154869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169109429062735,"score_gpt":0.2933725954004427,"score_spread":0.2816815011098153,"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."}}