{"id":"W3206705328","doi":"10.1039/d1lc00790d","title":"A microfluidic platform enables comprehensive gene expression profiling of mouse retinal stem cells","year":2021,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Bank of Canada; Krembil Foundation; Canada First Research Excellence Fund; Ontario Ministry of Research, Innovation and Science; National Cancer Institute; National Institutes of Health","keywords":"Stem cell; Microfluidics; Gene expression profiling; Gene expression; Retinal; Cell; Computational biology; Biology; RNA; Gene; Profiling (computer programming); Cell biology; Nanotechnology; Genetics; Computer science; Materials science; Biochemistry","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":[0.0001884629,0.000307594,0.0002256854,0.0003785397,0.0001680995,0.0002933521,0.0001921941,0.0002078098,0.0004708033],"category_scores_gemma":[0.0001394072,0.0001341848,0.0002015121,0.000162596,0.0001908705,0.0001434633,0.0002059318,0.0003310658,0.0001607296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125169,"about_ca_system_score_gemma":0.0003919396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004500066,"about_ca_topic_score_gemma":0.001049761,"domain_scores_codex":[0.9998651,0.000008653303,0.000008980505,0.0000485538,0.00004796909,0.00002083544],"domain_scores_gemma":[0.9999307,0.00001989835,0.00001945747,0.00000711318,0.00001114114,0.00001178601],"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":[0.00001194019,0.000004920565,0.0001068625,0.0000130038,0.000002508574,0.000007387206,0.000006216921,0.0001184042,0.9974947,0.0001312662,0.00004208523,0.002060807],"study_design_scores_gemma":[0.000004195384,0.00007189766,0.001735847,0.000002677586,0.000008919877,0.00002808853,0.000006567865,0.002105377,0.9931356,0.00008191183,0.00281176,0.000007278566],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7892454,0.001917572,0.1993365,0.0003137484,0.0001449302,0.0001904117,0.004319091,0.001981867,0.002550504],"genre_scores_gemma":[0.8430535,0.001396188,0.1492347,0.0001516803,0.0000355743,0.0005640284,0.001729599,0.0001229162,0.003711848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004708033,"threshold_uncertainty_score":0.00226754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02490276600047973,"score_gpt":0.2299204789909526,"score_spread":0.2050177129904729,"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."}}