{"id":"W2174601642","doi":"10.1186/s12859-015-0826-3","title":"MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions","year":2015,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Indel; INDEL Mutation; Sequence (biology); Genetics; Computational biology; Flanking maneuver; DNA microarray; Multiple sequence alignment; Alignment-free sequence analysis; Biology; Sequence analysis; Sequence alignment; 5' flanking region; Computer science; Gene; Peptide sequence; Single-nucleotide polymorphism; Engineering; Promoter; Genotype","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":[0.0002745701,0.000171469,0.0001349042,0.00006155612,0.0001390311,0.00005217724,0.0001623314,0.0001274083,5.420191e-7],"category_scores_gemma":[0.0002317679,0.0001571134,0.00007626339,0.00006885942,0.00004807006,0.000009006197,0.0001173958,0.00005435653,0.00001158208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006134735,"about_ca_system_score_gemma":0.0002040249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001529764,"about_ca_topic_score_gemma":0.00001790941,"domain_scores_codex":[0.9990577,0.0000153639,0.0003736733,0.0001138091,0.0001748603,0.0002646137],"domain_scores_gemma":[0.9991961,0.000015349,0.0002093724,0.0002991325,0.0001810034,0.00009907546],"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":[0.003122513,0.0007935126,0.04023405,0.002668067,0.001222563,0.000003293709,0.02090184,0.1869128,0.6629802,0.02599012,0.02875742,0.02641359],"study_design_scores_gemma":[0.004458325,0.0012443,0.0004209906,0.0001481573,0.00005248776,0.00003020817,0.00409891,0.8024953,0.06840478,0.00132138,0.1163829,0.0009422681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7059128,0.00008967661,0.2914006,0.00007683499,0.0001810487,0.001230415,0.0001270184,0.00001414089,0.0009674613],"genre_scores_gemma":[0.6333334,0.00001465606,0.3656487,0.0004654508,0.0001360842,0.0001244622,0.0001942655,0.00001639336,0.00006661062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6155825,"threshold_uncertainty_score":0.64069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1019180934086552,"score_gpt":0.291020284811656,"score_spread":0.1891021914030008,"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."}}