{"id":"W2411132881","doi":"10.1007/978-1-4939-3073-9_14","title":"Determination of the Substrate Specificity of Protein Kinases with Peptide Micro- and Macroarrays","year":2015,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Kinexus Bioinformatics Corporation (Canada)","funders":"","keywords":"In silico; Kinase; Peptide; Computational biology; Protein Array Analysis; Substrate specificity; DNA microarray; Phosphorylation; Substrate (aquarium); Biochemistry; Proteomics; Protein microarray; Computer science; Biology; Enzyme; Gene expression; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004096563,0.0005161795,0.0003347228,0.0002437143,0.0002076214,0.0005810562,0.0003324531,0.0003343787,0.0007297112],"category_scores_gemma":[0.0005386334,0.0002892422,0.0001732974,0.0002569067,0.0002524292,0.0004503134,0.000255128,0.0006134958,0.0005063837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000256024,"about_ca_system_score_gemma":0.0001666201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002398958,"about_ca_topic_score_gemma":0.0005177811,"domain_scores_codex":[0.9995744,0.00009182253,0.00002962235,0.0001336578,0.0001218955,0.00004859712],"domain_scores_gemma":[0.9996864,0.0001473704,0.00004397736,0.00003700085,0.00004423271,0.00004110615],"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":[0.00004842967,0.000004550399,0.0001105943,0.00002149722,0.000002847966,0.000009048221,0.000007565315,0.00006586045,0.9984785,0.0001124863,0.00002033752,0.001118282],"study_design_scores_gemma":[0.000001512808,0.00001619534,0.0004266968,0.000001057369,0.000002445262,0.0000397546,0.00000770736,0.0005942004,0.9984161,0.0000467477,0.0004451972,0.000002416893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9009582,0.004807498,0.08756148,0.0002421541,0.00009456342,0.00007061834,0.0006751701,0.0003286717,0.005261656],"genre_scores_gemma":[0.9400107,0.002559026,0.05305008,0.0001709006,0.00004139781,0.000115119,0.0006819317,0.00008583849,0.003285115],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0007297112,"threshold_uncertainty_score":0.002441108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472507732666261,"score_gpt":0.3571512447236528,"score_spread":0.3324261673969902,"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."}}