{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007167011,0.0002840028,0.0005386812,0.002200388,0.0005528719,0.0008295738,0.0006414378,0.0009988419,0.002707615],"category_scores_gemma":[0.003030285,0.0006236325,0.0004511128,0.002124829,0.000561135,0.001251119,0.0005321821,0.0005920632,0.001212838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003777353,"about_ca_system_score_gemma":0.0004297221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000564125,"about_ca_topic_score_gemma":0.001193723,"domain_scores_codex":[0.9995319,0.00007489631,0.00005124879,0.0001726927,0.0001149408,0.00005436661],"domain_scores_gemma":[0.9976372,0.0009643169,0.0005101701,0.0003408863,0.0003972368,0.0001502535],"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":[0.00289209,0.0002038799,0.01866657,0.0007060576,0.0001435714,0.002893815,0.000968364,0.03436683,0.7002682,0.03337357,0.002573034,0.202944],"study_design_scores_gemma":[0.0001640577,0.0003801306,0.06371196,0.0001423618,0.0001947052,0.005408337,0.0006951197,0.6021205,0.2186745,0.09676485,0.01162693,0.000116592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5950125,0.001299032,0.3974028,0.0002525273,0.00005733026,0.00008078457,0.0009018366,0.001434802,0.003558439],"genre_scores_gemma":[0.7932894,0.0003302501,0.2019528,0.00006332849,0.00003855468,0.00009665139,0.001949488,0.0003207117,0.001958905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002707615,"threshold_uncertainty_score":0.009057939,"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."}}