{"id":"W2294209416","doi":"10.1021/acs.analchem.5b04508","title":"Supramolecular Affinity Chromatography for Methylation-Targeted Proteomics","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Cancer-related gene regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Genome British Columbia; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada; University of Victoria","keywords":"Chemistry; Proteomics; Affinity chromatography; Proteome; Chromatography; Context (archaeology); Mass spectrometry; Supramolecular chemistry; Methylation; Computational biology; Combinatorial chemistry; Biochemistry; Organic chemistry; Molecule; DNA","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":[0.000505949,0.0005488995,0.0003648604,0.0005288346,0.0003531185,0.0003832516,0.0005250205,0.0004314166,0.00209847],"category_scores_gemma":[0.0005743357,0.0002191897,0.0002838168,0.0004392559,0.0002615833,0.0003205222,0.0004873712,0.0009344732,0.00167712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004288989,"about_ca_system_score_gemma":0.0004206723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002595131,"about_ca_topic_score_gemma":0.0007804266,"domain_scores_codex":[0.9995236,0.000110223,0.00002496653,0.00007842649,0.0002070302,0.00005555551],"domain_scores_gemma":[0.9997576,0.0001039753,0.0000310139,0.00003352705,0.00003691441,0.00003702036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003091264,0.00004547276,0.0001777977,0.0001261025,0.00001815746,0.00004988313,0.00001790862,0.0003511005,0.9815928,0.001162801,0.0005160115,0.01591105],"study_design_scores_gemma":[0.00001548899,0.0001045782,0.0008478038,0.00001441204,0.00001813746,0.0002595522,0.0000135798,0.006506351,0.978212,0.001312623,0.01267612,0.0000193537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2344653,0.01296719,0.7367843,0.001443354,0.0004091979,0.0005635361,0.0009588276,0.00216074,0.01024756],"genre_scores_gemma":[0.6477073,0.007165165,0.3341026,0.00107643,0.0001892516,0.0006346892,0.001218335,0.0001967383,0.007709507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00209847,"threshold_uncertainty_score":0.007020056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008736074630692807,"score_gpt":0.252136853706393,"score_spread":0.2434007790757002,"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."}}