{"id":"W3108440423","doi":"10.1093/bioinformatics/btaa976","title":"Deep feature extraction of single-cell transcriptomes by generative adversarial network","year":2020,"lang":"en","type":"article","venue":"Computer applications in the biosciences","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Douglas Mental Health University Institute; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Generative model; Generative grammar; Feature (linguistics); Source code; Embedding; Generative adversarial network; Raw data; Code (set theory); Artificial intelligence; Data mining; Machine learning; Deep learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001176639,0.0001052994,0.0001020893,0.00001706525,0.0001153368,0.00003473751,0.0004682392,0.00008334827,0.00000259533],"category_scores_gemma":[0.000004108499,0.00007741167,0.00006112002,0.0003274009,0.0001697292,0.000008168206,0.00002489714,0.00008197199,0.000001131314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005986997,"about_ca_system_score_gemma":0.00002629614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007948742,"about_ca_topic_score_gemma":0.00001296081,"domain_scores_codex":[0.9992223,0.00006683136,0.0001560205,0.0002875139,0.0001257648,0.0001415968],"domain_scores_gemma":[0.9996604,0.00002708376,0.00007369287,0.0001662871,0.00003470438,0.0000378616],"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":[0.00002500132,0.0001647775,0.001111779,0.00001085621,0.000007090963,1.616171e-7,0.0007132134,0.001937559,0.9877474,0.0001446154,0.005696903,0.002440648],"study_design_scores_gemma":[0.001208466,0.001203162,0.002070974,0.00001536157,0.00005411274,0.000007136056,0.0008875421,0.02712739,0.7567224,0.0003704657,0.2097836,0.0005493808],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.168782,0.001509135,0.8271095,0.001388925,0.0002691059,0.0004426335,0.00002124342,0.00001419139,0.0004632852],"genre_scores_gemma":[0.9864381,0.00008588042,0.01199874,0.0008222531,0.0005283279,0.00003028936,0.00007097887,0.000006074227,0.00001936978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8176562,"threshold_uncertainty_score":0.3156756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653542818112854,"score_gpt":0.2360269951309656,"score_spread":0.219491566949837,"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."}}