{"id":"W1975184116","doi":"10.1186/1756-0500-2-91","title":"KISSa: a strategy to build multiple sequence alignments from pairwise comparisons of very closely related sequences","year":2009,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Alignment-free sequence analysis; Pairwise comparison; Multiple sequence alignment; Sequence (biology); Computer science; Sequence alignment; Indel; Genome; Computational biology; Polyproteins; Selection (genetic algorithm); Tree (set theory); Process (computing); Data mining; Biology; Genetics; Artificial intelligence; Mathematics; Combinatorics; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005162976,0.0001657683,0.0002402507,0.00008258742,0.0001478294,0.00003431321,0.0004155708,0.0001340903,0.00001487787],"category_scores_gemma":[0.0006331858,0.0001522331,0.00008848665,0.0002285165,0.0001995795,0.000002136929,0.0001927678,0.0001451566,0.00002162933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002534997,"about_ca_system_score_gemma":0.0001986566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001215451,"about_ca_topic_score_gemma":0.0004342048,"domain_scores_codex":[0.9981616,0.0002276742,0.000318694,0.0004765614,0.0003658826,0.0004496602],"domain_scores_gemma":[0.9988359,0.0002357418,0.00007095513,0.0004559031,0.000238348,0.0001631225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001406896,0.0001040085,0.08118123,0.000009303889,0.00006156915,0.000003828369,0.0001229812,0.0009810789,0.9145046,0.00009611931,0.001093319,0.001701303],"study_design_scores_gemma":[0.0007892746,0.001999705,0.2641465,0.00006183663,0.00001691807,0.000002779926,0.0004439607,0.0004343652,0.725993,0.002752109,0.003038048,0.00032149],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964632,0.001961887,0.0002480147,0.0002616965,0.00005661212,0.0003651479,0.000166114,0.00000590372,0.0004713611],"genre_scores_gemma":[0.9950176,0.0003488967,0.00426706,0.0000603553,0.00009036316,0.00002559089,0.00006396134,0.0000130957,0.0001130503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1885116,"threshold_uncertainty_score":0.6207885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1637722580268993,"score_gpt":0.3920548699928228,"score_spread":0.2282826119659235,"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."}}