{"id":"W2106014491","doi":"10.1155/bsb/2006/35809","title":"Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Bioinformatics and Systems Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Sequence (biology); Computational biology; Relevance (law); Consensus sequence; Computer science; Biology; Data mining; Bioinformatics; Genetics; Gene; Artificial intelligence; Peptide sequence","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":[0.0006106504,0.0002256113,0.000261142,0.0001665195,0.0001861839,0.0001142725,0.0001183513,0.0001938717,0.000001816883],"category_scores_gemma":[0.0001317565,0.0001586471,0.00004438343,0.00007331576,0.0001629302,0.00001302553,0.00005934888,0.0001802985,0.000004311101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000223639,"about_ca_system_score_gemma":0.00003811672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001130642,"about_ca_topic_score_gemma":0.00007823019,"domain_scores_codex":[0.9985537,0.0001889225,0.0007409283,0.0001481147,0.00008687363,0.0002814735],"domain_scores_gemma":[0.9991478,0.00007553925,0.0004570247,0.0001593649,0.00007279563,0.00008750209],"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":[0.0006380397,0.0002694031,0.3517915,0.0008198263,0.0002696903,0.0000431098,0.00332356,0.004321895,0.6097381,0.006409152,0.00302934,0.01934639],"study_design_scores_gemma":[0.02297051,0.01063627,0.08702878,0.002037254,0.00009088474,0.01214957,0.02258024,0.6572934,0.06027544,0.003165027,0.1174468,0.004325835],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935472,0.0005137852,0.004283294,0.0003172335,0.0001916107,0.0004277924,0.00005085401,0.00001260519,0.0006556393],"genre_scores_gemma":[0.9967714,0.0001478279,0.002486673,0.0001735678,0.0001471889,0.0000171007,0.0001129483,0.00001269342,0.0001306087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6529715,"threshold_uncertainty_score":0.646944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611228136636599,"score_gpt":0.2540163877712474,"score_spread":0.2379041064048814,"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."}}