{"id":"W4321505365","doi":"10.1039/d3sc00560g","title":"All-in-One digital microfluidics pipeline for proteomic sample preparation and analysis","year":2023,"lang":"en","type":"article","venue":"Chemical Science","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Cancer Agency; University of Saskatchewan; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Pipeline (software); Proteome; Sample preparation; Proteomics; Chemistry; Microfluidics; Chromatography; Computational biology; Bioinformatics; Computer science; Biology; Biochemistry; Nanotechnology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002073371,0.00006815193,0.0001077782,0.000183685,0.00003717543,0.00006275043,0.0001618766,0.00004990794,0.000001068974],"category_scores_gemma":[0.0004094274,0.00006674467,0.00002649535,0.001398919,0.0001535952,0.0001327012,0.00005310613,0.00006512937,0.000003854135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006085102,"about_ca_system_score_gemma":0.00001573528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000643971,"about_ca_topic_score_gemma":8.037991e-7,"domain_scores_codex":[0.9992791,0.000001165008,0.0001261596,0.0002142273,0.00008679889,0.0002924925],"domain_scores_gemma":[0.9997371,0.00007054238,0.0000122234,0.0001204095,0.00002196562,0.00003780405],"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":[0.000002626281,0.000005156352,0.0008075991,0.0000130463,0.000007728261,1.757862e-7,0.00006005185,0.0000287695,0.990799,0.00004154849,0.0005418917,0.007692374],"study_design_scores_gemma":[0.000097431,0.00000897253,0.0003878718,0.000005969673,0.00001513568,7.094522e-7,0.00001517228,0.01802591,0.9796046,0.0009787484,0.0007724345,0.00008706326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823571,0.0002014652,0.01655822,0.0001654521,0.00001786052,0.0001541796,0.00001928019,0.0004788725,0.00004755331],"genre_scores_gemma":[0.9979554,0.0001170574,0.001804123,0.00001109513,0.00001350689,0.00003898653,0.00003319316,0.000006003673,0.00002056778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01799714,"threshold_uncertainty_score":0.2721769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103469737105411,"score_gpt":0.2677760378496928,"score_spread":0.2467413404786387,"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."}}