{"id":"W2076795608","doi":"10.1142/s0219720008003527","title":"MANGO: MULTIPLE ALIGNMENT WITH N GAPPED OLIGOS","year":2008,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Multiple sequence alignment; Heuristics; Scalability; Computer science; Dynamic programming; Sequence (biology); Sequence alignment; Task (project management); State (computer science); Algorithm; Biology","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":[0.0008737444,0.001791408,0.001009696,0.0007014495,0.0008924068,0.0009875882,0.001453234,0.001013817,0.005902051],"category_scores_gemma":[0.002455741,0.0009569715,0.0009382846,0.0009823439,0.0005901013,0.001737072,0.00144241,0.001515298,0.003930554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00060641,"about_ca_system_score_gemma":0.001051894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139029,"about_ca_topic_score_gemma":0.002904055,"domain_scores_codex":[0.9993107,0.0001431411,0.00007160423,0.0002861095,0.0001302812,0.00005815196],"domain_scores_gemma":[0.9994592,0.000233035,0.00009504073,0.0001148521,0.00005031,0.00004757303],"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":[0.004240675,0.0003959443,0.00830255,0.005203562,0.0007337775,0.001315461,0.000946831,0.04556556,0.3387665,0.02640023,0.1287782,0.4393507],"study_design_scores_gemma":[0.0006719849,0.0008086722,0.004931615,0.0003442915,0.0002776893,0.001535397,0.0004122395,0.3711448,0.2880802,0.03803602,0.2934176,0.0003394902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06904059,0.003726095,0.7485019,0.0006204886,0.0007185199,0.0006255956,0.01125647,0.1545876,0.01092276],"genre_scores_gemma":[0.1060652,0.0008516893,0.8653125,0.0004546014,0.0000655402,0.0008754552,0.01283939,0.00839268,0.005143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005902051,"threshold_uncertainty_score":0.01974434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105151779553291,"score_gpt":0.2176171667524678,"score_spread":0.2071019887971387,"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."}}