{"id":"W4220952572","doi":"10.18280/isi.270108","title":"Semi Global Pairwise Sequence Alignment Using New Chromosome Structure Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Alignment-free sequence analysis; Sequence (biology); Pairwise comparison; Chromosome; Multiple sequence alignment; Algorithm; Sequence alignment; Computer science; Task (project management); Genetic algorithm; DNA sequencing; Structural alignment; Biological data; Artificial intelligence; Computational biology; Genetics; DNA; Biology; Machine learning; Peptide sequence; Gene; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006412275,0.00074967,0.0009173239,0.001266619,0.0005358237,0.0006308432,0.001079985,0.0008883042,0.001573366],"category_scores_gemma":[0.001325795,0.0003479931,0.0007673366,0.001109211,0.0005501549,0.001052461,0.0008007266,0.0008835808,0.0003881799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005948637,"about_ca_system_score_gemma":0.001182211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002694599,"about_ca_topic_score_gemma":0.003018638,"domain_scores_codex":[0.9993813,0.0001794204,0.00002842445,0.0001802631,0.0001924677,0.00003798914],"domain_scores_gemma":[0.9995372,0.0002083071,0.00006779569,0.00005402042,0.0001091578,0.00002357278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002328448,0.0001810764,0.002580575,0.0001663076,0.0001588076,0.0002463256,0.0002866268,0.5575482,0.0426412,0.01734415,0.002548465,0.3760654],"study_design_scores_gemma":[0.00005728509,0.0001213389,0.0005504776,0.00001193684,0.00002733298,0.0001545627,0.00003426376,0.9844169,0.005545218,0.006665402,0.002393614,0.00002161738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01837769,0.000122488,0.9799248,0.0000556936,0.00002343128,0.00005891219,0.00004817921,0.0007035091,0.0006853301],"genre_scores_gemma":[0.1143607,0.0001199659,0.883796,0.00005826328,0.0000163841,0.0001762137,0.0003509024,0.0001168515,0.001004689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002694599,"threshold_uncertainty_score":0.005357862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143606967519171,"score_gpt":0.2345887290322843,"score_spread":0.2202280322803672,"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."}}