{"id":"W2027893274","doi":"10.1371/journal.pone.0011082","title":"Identifying and Seeing beyond Multiple Sequence Alignment Errors Using Intra-Molecular Protein Covariation","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multiple sequence alignment; Sequence alignment; Sequence (biology); Alignment-free sequence analysis; Parametric statistics; Computer science; Computational biology; Structural alignment; Biology; Sequence analysis; Principal component analysis; Statistics; Genetics; Artificial intelligence; Mathematics; 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":[0.0001395467,0.0001290454,0.0001181703,0.00003378148,0.0001027935,0.0000460579,0.0001004435,0.0001539731,0.000006616181],"category_scores_gemma":[0.0001213588,0.0001370066,0.0000268873,0.00005674203,0.00005941082,0.000009804286,0.0001075851,0.0001469532,0.00000146951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001471019,"about_ca_system_score_gemma":0.00004077102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004608635,"about_ca_topic_score_gemma":0.00005204366,"domain_scores_codex":[0.9991446,0.00003082662,0.0001555001,0.000301382,0.0001756809,0.000192011],"domain_scores_gemma":[0.9995269,0.000004754864,0.00008827153,0.000248191,0.00006233019,0.0000695748],"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":[0.00001827255,0.00005331945,0.001801366,0.00005160224,0.00007001348,0.000005562554,0.00005895129,0.00001936776,0.997187,0.0003438157,8.238001e-7,0.0003898712],"study_design_scores_gemma":[0.0003040194,0.00005186905,0.0003897955,0.0000430096,0.00004718597,0.00001002273,0.00002198067,0.004677678,0.9929918,0.001251447,0.00003231287,0.0001788716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817427,0.0001360636,0.01750209,0.00006039914,0.00004774361,0.0004021688,0.00001340962,0.00001525683,0.00008017401],"genre_scores_gemma":[0.9226904,0.00001059527,0.07695964,0.0001108679,0.00009286778,0.00002517716,0.00005787592,0.0000198701,0.00003271345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05945755,"threshold_uncertainty_score":0.5586966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02527849425547806,"score_gpt":0.2492929282177352,"score_spread":0.2240144339622571,"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."}}