{"id":"W3098600211","doi":"10.1038/s41467-020-19394-5","title":"Digital microfluidic isolation of single cells for -Omics","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":178,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; Canada First Research Excellence Fund; University of Toronto; Massachusetts Institute of Technology","keywords":"Computational biology; Microfluidics; Context (archaeology); Transcriptome; Proteome; Cell; Single-cell analysis; Isolation (microbiology); DNA sequencing; Population; Biology; Computer science; Bioinformatics; DNA; Genetics; Nanotechnology; Gene; Gene expression","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":[0.00003526023,0.00007243287,0.00008715294,0.00001513371,0.0000665269,0.00001893834,0.0004252594,0.0001999214,0.000001332034],"category_scores_gemma":[0.00008806516,0.0000767302,0.00008796491,0.00007509393,0.00006716886,0.000005120273,0.00008596458,0.0001318151,0.000001793591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000632786,"about_ca_system_score_gemma":0.00003337026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001251047,"about_ca_topic_score_gemma":0.000006753603,"domain_scores_codex":[0.9995813,0.00001631812,0.0001575893,0.0001233234,0.00004176131,0.00007969246],"domain_scores_gemma":[0.9992585,0.00003485754,0.00006635618,0.0004752127,0.0001244954,0.00004063176],"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":[0.00004250754,0.00008774687,0.0005483569,0.00001266741,0.00001992732,1.776551e-8,0.00006135917,0.000004991232,0.9929487,0.0001585058,0.005018453,0.001096775],"study_design_scores_gemma":[0.0002985978,0.0001582878,0.0000760248,0.000004290996,0.00001620892,5.144153e-7,0.00003118279,0.0004013399,0.7028184,0.00003031951,0.2960791,0.00008573313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7371514,0.05232044,0.1808602,0.01468516,0.0008064316,0.001827462,0.002726717,0.0001170794,0.009505119],"genre_scores_gemma":[0.9906008,0.0005205598,0.007219896,0.0005748234,0.00007933729,0.000008148788,0.0009156727,0.00001606145,0.00006470001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2910606,"threshold_uncertainty_score":0.3128967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02551723762695977,"score_gpt":0.2559366431350373,"score_spread":0.2304194055080775,"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."}}