{"id":"W3198446608","doi":"10.1371/journal.pone.0257232","title":"PIIKA 2.5: Enhanced quality control of peptide microarrays for kinome analysis","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Infectious Diseases Centre; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Kinome; Computer science; Metric (unit); Data mining; Computational biology; Identification (biology); Software; Biology; Kinase","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":[0.0000921317,0.00007851645,0.000238324,0.00002460478,0.0000401097,0.000005566798,0.00008154302,0.00007584234,0.000009538299],"category_scores_gemma":[0.0001230753,0.00008232117,0.0001634398,0.0001587037,0.0000448319,0.000001728083,0.00003141318,0.00003305502,9.083703e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007344432,"about_ca_system_score_gemma":0.0000332193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005546576,"about_ca_topic_score_gemma":0.00002204691,"domain_scores_codex":[0.9993063,0.00002567655,0.0002164069,0.0002548902,0.00007388896,0.0001228978],"domain_scores_gemma":[0.9991287,0.00002694029,0.0001185153,0.0003971417,0.0002934872,0.00003521343],"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":[0.00003852174,0.0003622636,0.0002686532,0.00003104817,0.0005727076,1.259768e-7,0.000006413653,0.00001221916,0.998061,0.0004008349,0.0000466494,0.000199573],"study_design_scores_gemma":[0.0003153328,0.00006464841,0.0007428239,0.00001081799,0.0003698573,2.237431e-7,0.00001732743,0.00006535886,0.9969775,0.0003611797,0.0009726841,0.000102226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6189708,0.0001952362,0.3799603,0.0003076795,0.000004698813,0.0001883467,0.0001715469,0.00001547199,0.0001859515],"genre_scores_gemma":[0.9092716,0.00008293927,0.0894433,0.000236923,0.00005619545,0.00005023063,0.00030012,0.000009525077,0.0005491482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.290517,"threshold_uncertainty_score":0.335696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04270922586165352,"score_gpt":0.2931224173930398,"score_spread":0.2504131915313863,"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."}}