{"id":"W4385978018","doi":"10.1016/j.crmeth.2023.100563","title":"Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures","year":2023,"lang":"en","type":"article","venue":"Cell Reports Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Omics; Leverage (statistics); Computational biology; Coronavirus disease 2019 (COVID-19); Computer science; Embedding; Data type; Biology; Bioinformatics; Artificial intelligence; Disease; Medicine; Infectious disease (medical specialty)","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":[0.0009069835,0.0007203282,0.0007187524,0.001171857,0.0002420426,0.0007970158,0.0004114206,0.0007042294,0.001067131],"category_scores_gemma":[0.001442584,0.0001867612,0.001112463,0.0009242116,0.0003073572,0.0005651728,0.0008451017,0.0008829858,0.0004816403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004137888,"about_ca_system_score_gemma":0.0005529322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001862923,"about_ca_topic_score_gemma":0.003249602,"domain_scores_codex":[0.9996815,0.00006015804,0.00002034256,0.0001310503,0.00004785767,0.0000590738],"domain_scores_gemma":[0.9996095,0.0001779955,0.00006784768,0.00004681095,0.0000607113,0.00003719349],"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":[0.002028189,0.0005625164,0.1971639,0.001352084,0.001368145,0.001195432,0.0006215401,0.09036476,0.3507503,0.006276111,0.01903658,0.3292803],"study_design_scores_gemma":[0.00008018231,0.0003203542,0.1017907,0.0001169163,0.0003925746,0.0006578559,0.0005343462,0.811169,0.05645846,0.01556613,0.01280317,0.0001104417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6448416,0.005905502,0.3296413,0.001186287,0.0002852936,0.0001277137,0.01387037,0.001508522,0.002633348],"genre_scores_gemma":[0.9193563,0.001238324,0.06217316,0.0002835113,0.0001366117,0.0001449519,0.01437794,0.00009244555,0.002196652],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001862923,"threshold_uncertainty_score":0.004796684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04325176941931279,"score_gpt":0.3302967204911473,"score_spread":0.2870449510718345,"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."}}