{"id":"W1986902347","doi":"10.1002/prot.22723","title":"The evolutionary landscape of the chromatin modification machinery reveals lineage specific gains, expansions, and losses","year":2010,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Lineage (genetic); Biology; Modularity (biology); Context (archaeology); Evolutionary biology; Function (biology); Set (abstract data type); Computational biology; Repertoire; Chromatin; Gene; Genetics; Computer science","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":[0.0001450801,0.0001259654,0.00009361136,0.0000244476,0.0003441712,0.00005110825,0.0001288257,0.0001373988,0.000008964225],"category_scores_gemma":[0.00006907497,0.00007034015,0.0000397809,0.00007421699,0.0001632213,0.000009954849,0.000111885,0.0001619758,6.559335e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004020256,"about_ca_system_score_gemma":0.00003653222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003645492,"about_ca_topic_score_gemma":0.0000302798,"domain_scores_codex":[0.9993243,0.00002593348,0.0002963774,0.0001162067,0.0001199409,0.0001172681],"domain_scores_gemma":[0.9992871,0.00001739895,0.0002233508,0.0003516039,0.00007902773,0.00004154563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006928846,0.00002292457,0.007677889,0.0001531655,0.00003951989,6.982425e-8,0.0001562058,0.00007086972,0.9752911,0.00281852,0.001688716,0.01201176],"study_design_scores_gemma":[0.002941669,0.0008440671,0.6937419,0.0001442636,0.0001509547,0.0003327847,0.002385359,0.09877031,0.1196066,0.008877668,0.07107116,0.001133304],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960285,0.0007396952,0.002042107,0.0002219717,0.0002763507,0.0003920164,0.00008386844,0.000008655272,0.0002067834],"genre_scores_gemma":[0.9922988,0.000524596,0.006707232,0.00008432788,0.0001368476,0.00001122008,0.0001112587,0.000009136914,0.0001165834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8556845,"threshold_uncertainty_score":0.2868388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005624776859677908,"score_gpt":0.2051930675175853,"score_spread":0.1995682906579074,"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."}}