{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008348607,0.001256488,0.0008273579,0.001011601,0.0005148501,0.0009443518,0.001521729,0.0007409002,0.007612981],"category_scores_gemma":[0.00108115,0.0007043271,0.000601928,0.0003657622,0.00042048,0.0009739019,0.001779233,0.0008009337,0.002998319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007214972,"about_ca_system_score_gemma":0.001442686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005790035,"about_ca_topic_score_gemma":0.001294133,"domain_scores_codex":[0.9990584,0.00006019507,0.00006747574,0.0003173357,0.0004018125,0.00009467386],"domain_scores_gemma":[0.9993519,0.0001491335,0.00009892926,0.0001751336,0.0001472774,0.0000776364],"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":[0.0003986793,0.0001361376,0.001767814,0.0005978565,0.0001065928,0.0001744332,0.000108336,0.001252258,0.8867974,0.002811542,0.01290063,0.0929485],"study_design_scores_gemma":[0.00008390553,0.0003574758,0.00315392,0.00004959811,0.0001005759,0.0007326475,0.00003172128,0.02538617,0.8619105,0.001811538,0.1062598,0.0001221333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08335421,0.00237361,0.8593122,0.001082183,0.0009928134,0.001083312,0.008242959,0.03539425,0.008164448],"genre_scores_gemma":[0.2474979,0.002162572,0.7177795,0.001807274,0.0003566193,0.002527819,0.01083144,0.001826907,0.01520999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007612981,"threshold_uncertainty_score":0.02546793,"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."}}