{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005316173,0.0001471488,0.0001816883,0.00002542983,0.00005811945,0.00001552675,0.0001215904,0.0001415922,0.00001770691],"category_scores_gemma":[0.000007729326,0.0001347016,0.00009818562,0.0000623158,0.00004508694,0.000002630534,0.00006028001,0.0001019074,0.000006964059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001002154,"about_ca_system_score_gemma":0.00007399028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009035525,"about_ca_topic_score_gemma":0.000003039883,"domain_scores_codex":[0.9991154,0.0000449176,0.0002155738,0.0003078341,0.0001222926,0.0001939678],"domain_scores_gemma":[0.9994263,0.00001587615,0.0000781609,0.0003002991,0.0001177524,0.00006161306],"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":[0.0002539006,0.0001310488,0.0004408645,0.00008885394,0.0000250613,0.0000126874,0.00008055349,0.00001696802,0.997914,0.00004283577,0.0006209391,0.0003723113],"study_design_scores_gemma":[0.0006900601,0.0002076713,0.00005107715,0.0000630953,0.00001199965,0.00001072894,0.000160649,0.00001479575,0.9933777,0.00001357291,0.005250226,0.0001483879],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956451,0.003109529,0.0004613053,0.00002085075,0.0001500979,0.0001207724,0.00006290182,0.00001205764,0.0004173543],"genre_scores_gemma":[0.9946251,0.001298659,0.002294881,0.0002889685,0.0001279675,0.000007547778,0.0001531295,0.00002930579,0.00117447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004629287,"threshold_uncertainty_score":0.5492971,"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."}}