{"id":"W2026060326","doi":"10.1109/tkde.2012.151","title":"NHOP: A Nested Associative Pattern for Analysis of Consensus Sequence Ensembles","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Associative property; Computer science; Sequence (biology); Tree (set theory); Theoretical computer science; Tree structure; Pattern recognition (psychology); Core (optical fiber); Artificial intelligence; Algorithm; Data mining; Computational biology; Mathematics; Combinatorics; Biology; Binary tree","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":[0.0001680547,0.00009673585,0.000155096,0.00008108337,0.00004167971,0.000008057676,0.00009950094,0.00008421428,0.00000298354],"category_scores_gemma":[0.000008163556,0.00009480254,0.00006018289,0.0001326984,0.00001931988,0.000007161093,0.000005411844,0.00005347616,9.486621e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008286624,"about_ca_system_score_gemma":0.00001651364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000082343,"about_ca_topic_score_gemma":0.0000480399,"domain_scores_codex":[0.9995021,0.000009732212,0.0001631892,0.0001324014,0.0000333758,0.000159188],"domain_scores_gemma":[0.9995124,0.00006315369,0.00004713612,0.0002752618,0.00004345593,0.0000585782],"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":[0.0002939531,0.0009800531,0.003163287,0.000882334,0.01328385,0.000001575903,0.004481518,0.08711105,0.4779567,0.0002612864,0.003843951,0.4077405],"study_design_scores_gemma":[0.000938107,0.0002126853,0.001682298,0.00005823963,0.001581176,0.000008011699,0.0002275159,0.9186438,0.06576114,0.000005748914,0.01038985,0.000491471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1196593,0.0006443176,0.8782077,0.00001225847,0.0001710396,0.0001113919,0.001142238,0.000008764941,0.00004290664],"genre_scores_gemma":[0.9981168,0.0001113231,0.001362209,0.00001844282,0.00005884552,0.00001250737,0.0002619461,0.00000972885,0.00004821846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8784574,"threshold_uncertainty_score":0.3865936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03855882831999959,"score_gpt":0.2947681018071184,"score_spread":0.2562092734871188,"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."}}