{"id":"W2467788496","doi":"10.1039/c6an00729e","title":"A “chemical nose” biosensor for detecting proteins in complex mixtures","year":2016,"lang":"en","type":"article","venue":"The Analyst","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; National Institute for Nanotechnology; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Bovine serum albumin; Lysozyme; Biosensor; Antibody; Chromatography; Immunoassay; Human serum albumin; Proteomics; Flow cytometry; Blood proteins; Detection limit; Colloidal gold; Biochemistry; Nanoparticle; Molecular biology; Nanotechnology; Biology; Immunology; Materials 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005625592,0.0005919749,0.0003887164,0.0005066681,0.000298571,0.0003965085,0.001012043,0.001688336,0.000677869],"category_scores_gemma":[0.0007011216,0.0003579893,0.000385114,0.0002252477,0.0005822939,0.0006882348,0.000458444,0.0008709937,0.0006085186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003388287,"about_ca_system_score_gemma":0.0006332371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003713755,"about_ca_topic_score_gemma":0.0008436,"domain_scores_codex":[0.9992298,0.0001429516,0.00003052259,0.0001637364,0.0003888613,0.00004415235],"domain_scores_gemma":[0.9997166,0.0001048743,0.00002824696,0.00002269235,0.00009459439,0.00003294408],"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":[0.00004632486,0.00003017899,0.0001278673,0.00008440443,0.000005621961,0.00008654978,0.00002144162,0.0001378465,0.9874932,0.001067695,0.0004466481,0.01045217],"study_design_scores_gemma":[0.00001882794,0.0002287402,0.0004562678,0.0000151301,0.00001556292,0.0009728669,0.00001803553,0.01235498,0.9786118,0.0005043076,0.006774796,0.0000288013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1920625,0.005062077,0.784074,0.002544286,0.001783081,0.0005829596,0.0003856153,0.003736383,0.009769051],"genre_scores_gemma":[0.4031473,0.002340535,0.581175,0.002890776,0.0001774385,0.0004077524,0.0002619174,0.00009092877,0.009508303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001688336,"threshold_uncertainty_score":0.002975166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990391293527505,"score_gpt":0.2922063043782475,"score_spread":0.2723023914429725,"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."}}