{"id":"W2005513967","doi":"10.1039/c0lc00291g","title":"Hydrogel droplet microarrays with trapped antibody-functionalized beads for multiplexed protein analysis","year":2010,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Multiplex; Protein microarray; Microbead (research); Antibody microarray; Confocal microscopy; Chromatography; Chemistry; Substrate (aquarium); Confocal; Microsphere; Microarray; Materials science; Nanotechnology; Antibody; Biochemistry; Chemical engineering; Biology; Gene expression; Bioinformatics; Cell biology","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":[0.00008891194,0.0001571023,0.0001606932,0.00006143254,0.0001315292,0.00001826406,0.0001237051,0.0001373649,0.00001770401],"category_scores_gemma":[0.00003145528,0.0001260196,0.0001415147,0.0002279859,0.00008161178,0.000002144274,0.00002118434,0.0001111441,0.00000299745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007352031,"about_ca_system_score_gemma":0.00003158148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001534268,"about_ca_topic_score_gemma":0.0001693529,"domain_scores_codex":[0.9991822,0.00001157292,0.0001398931,0.0003901017,0.00008507213,0.0001911612],"domain_scores_gemma":[0.9993078,0.00001168849,0.00008892872,0.0004411891,0.00008671454,0.00006364512],"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":[0.0002528218,0.0001097008,0.0006203761,0.000005570186,0.0001305404,4.283524e-7,0.000005345676,0.00001498201,0.9961594,0.00184413,0.0001838653,0.0006728307],"study_design_scores_gemma":[0.001028826,0.0003365133,0.001362596,0.000008468067,0.000114628,0.000005582074,0.000007224253,0.0004214873,0.9049832,0.0004147179,0.09105912,0.0002576614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9415861,0.00001332226,0.05676422,0.0003449656,0.00002087001,0.0006909179,0.0001121426,0.00006421389,0.0004032231],"genre_scores_gemma":[0.9203694,0.000004186147,0.0768473,0.0003052276,0.0001164001,0.0002027574,0.00104283,0.00002689716,0.001085002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09117623,"threshold_uncertainty_score":0.5138929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008059679925646714,"score_gpt":0.2753266873579014,"score_spread":0.2672670074322547,"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."}}