{"id":"W2003722503","doi":"10.1108/ec-01-2013-0026","title":"A multiple sequence alignment method with sequence vectorization","year":2014,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Multiple sequence alignment; Alignment-free sequence analysis; Sequence (biology); Vectorization (mathematics); Computer science; Sequence alignment; Scale (ratio); Tree (set theory); Algorithm; Data mining; Parallel computing; Mathematics; Biology; Peptide sequence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006625531,0.001233781,0.0008256886,0.001529094,0.0008379271,0.001162257,0.001613758,0.0009304876,0.007422957],"category_scores_gemma":[0.001954951,0.0005279968,0.001045582,0.002206302,0.0006498537,0.001857162,0.001057413,0.001566914,0.004634117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007666002,"about_ca_system_score_gemma":0.001347675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001925806,"about_ca_topic_score_gemma":0.001442266,"domain_scores_codex":[0.9987094,0.0002760961,0.0001150914,0.0003243728,0.0005000714,0.00007498293],"domain_scores_gemma":[0.999469,0.0001092533,0.00007211134,0.00007685368,0.000240271,0.00003250902],"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":[0.0002447978,0.0001079409,0.0008867389,0.0009835792,0.0001397929,0.0003265607,0.0003902159,0.03237411,0.1042647,0.04167113,0.01778842,0.800822],"study_design_scores_gemma":[0.0001174312,0.0002861029,0.0007934198,0.0001347081,0.00007012163,0.001247417,0.0002047753,0.7441524,0.08449877,0.02206826,0.1462853,0.0001413276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001292497,0.000258216,0.994517,0.00007139468,0.00009128916,0.00006843054,0.00007081247,0.002229516,0.001400827],"genre_scores_gemma":[0.02696148,0.000389984,0.9677106,0.0001159684,0.0000561823,0.0002772607,0.0004876459,0.0004286078,0.003572307],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007422957,"threshold_uncertainty_score":0.02483225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01353865683233167,"score_gpt":0.2472042008016023,"score_spread":0.2336655439692706,"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."}}