{"id":"W2131848438","doi":"10.1093/bioinformatics/bti671","title":"Using information theory to search for co-evolving residues in proteins","year":2005,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":287,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multiple sequence alignment; Phylogenetic tree; Computational biology; Computer science; Normalization (sociology); In silico; Sequence alignment; Protein sequencing; Conserved sequence; Mutual information; Biology; Genetics; Peptide sequence; Artificial intelligence; Gene","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":[0.004770319,0.0008417631,0.001329943,0.005749028,0.0007243569,0.001648716,0.001192079,0.001475356,0.0009448359],"category_scores_gemma":[0.0201323,0.0006017356,0.0008842857,0.00259428,0.001700587,0.002729758,0.001643585,0.001032528,0.0002531351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543538,"about_ca_system_score_gemma":0.0009468352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001244786,"about_ca_topic_score_gemma":0.0009953898,"domain_scores_codex":[0.9980661,0.0009962957,0.0001097594,0.0002566006,0.0004797241,0.00009142693],"domain_scores_gemma":[0.9831955,0.01474629,0.0009922134,0.0003849978,0.0004401065,0.0002408662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000546952,0.0002887757,0.02114863,0.0006656888,0.0005939881,0.0006089468,0.000332766,0.7665965,0.01025378,0.05353377,0.001634823,0.1437953],"study_design_scores_gemma":[0.00001758273,0.00004179309,0.00115633,0.00001288826,0.00002293169,0.0000657045,0.00001924874,0.9622241,0.001623369,0.03456587,0.0002306871,0.00001962428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1521314,0.001106166,0.843144,0.0006513158,0.00002998722,0.00009839926,0.0002687188,0.0008856619,0.001684386],"genre_scores_gemma":[0.7353771,0.0005275963,0.2624266,0.0001747861,0.0000858431,0.0002408257,0.000631579,0.00008645922,0.0004492156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005749028,"threshold_uncertainty_score":0.0252282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02920641743422231,"score_gpt":0.2963293837242249,"score_spread":0.2671229662900026,"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."}}