{"id":"W2952227234","doi":"10.1371/journal.pcbi.1007113","title":"Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes","year":2019,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Vetenskapsrådet; National Institutes of Health; Kids Brain Health Network; Science for Life Laboratory","keywords":"Electrophysiology; Biology; Excitatory postsynaptic potential; Transcriptome; Neuroscience; Gene expression; Inhibitory postsynaptic potential; Phenotype; Gene; Gene expression profiling; Cell type; Function (biology); Evolutionary biology; Computational biology; Cell; Genetics","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":[0.0006118766,0.0002679072,0.0005998228,0.0009186754,0.0003393827,0.0009510725,0.0002574832,0.0003771623,0.001452355],"category_scores_gemma":[0.002460267,0.0001756097,0.0004364665,0.001193542,0.0006153707,0.0005598187,0.0005812415,0.0006487654,0.000452384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003158886,"about_ca_system_score_gemma":0.0003340741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001051831,"about_ca_topic_score_gemma":0.002405243,"domain_scores_codex":[0.9994379,0.0000507866,0.00003941456,0.0002835692,0.00008969323,0.00009858862],"domain_scores_gemma":[0.9984117,0.0007626664,0.0003174092,0.0001794021,0.0002154673,0.0001133617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000405847,0.00006883581,0.2129042,0.0003269748,0.0002796028,0.000197124,0.0004286578,0.002300819,0.7631773,0.0009931382,0.0006213904,0.01829605],"study_design_scores_gemma":[0.00001160773,0.00009369403,0.963983,0.00002813602,0.0001569366,0.0003054895,0.0003220961,0.007212796,0.02405649,0.001715305,0.002074867,0.00003948025],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700124,0.0007270185,0.0149885,0.0001329396,0.00003174461,0.00003324232,0.01212822,0.000211695,0.0017342],"genre_scores_gemma":[0.980678,0.0003256713,0.007017654,0.0001375341,0.00002530169,0.0001407698,0.01079375,0.0001105529,0.0007707334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001452355,"threshold_uncertainty_score":0.004858613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009442745321803085,"score_gpt":0.2398125941255571,"score_spread":0.230369848803754,"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."}}