{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005413553,0.001328818,0.001007042,0.002315732,0.0005218531,0.00210303,0.00189164,0.0009579207,0.007218056],"category_scores_gemma":[0.01156148,0.001258226,0.001171533,0.001327369,0.0004793902,0.001688301,0.001538751,0.002295446,0.002800667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008789519,"about_ca_system_score_gemma":0.000858422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001133447,"about_ca_topic_score_gemma":0.001690693,"domain_scores_codex":[0.9968245,0.0004550836,0.0002964473,0.0006182078,0.001588631,0.0002170452],"domain_scores_gemma":[0.994409,0.002294153,0.000712385,0.0009658554,0.001435566,0.0001829814],"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":[0.002659731,0.0003642055,0.01450726,0.001309976,0.0006352413,0.0006218067,0.0008657138,0.01511612,0.5940238,0.00714943,0.07207494,0.2906717],"study_design_scores_gemma":[0.0002190412,0.0004368145,0.0280608,0.000104501,0.0002112476,0.001146629,0.000121378,0.2558846,0.6373317,0.008286809,0.06770011,0.0004963872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04617265,0.0005794046,0.7955833,0.0003367499,0.0002888379,0.000441127,0.005253266,0.1482527,0.003092043],"genre_scores_gemma":[0.1354874,0.000461432,0.8259907,0.0007043601,0.0001233058,0.002150952,0.01298616,0.01700091,0.005094865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007218056,"threshold_uncertainty_score":0.0286299,"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."}}