{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001632744,0.0003766207,0.0004526093,0.0001394988,0.0002739215,0.00009010953,0.000215407,0.0005858513,0.00005160712],"category_scores_gemma":[0.0003684498,0.0003820506,0.0002037145,0.0003661418,0.0001533024,0.00001025876,0.0001969779,0.0003692966,0.000007848442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005682895,"about_ca_system_score_gemma":0.000145057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000391059,"about_ca_topic_score_gemma":0.000003292643,"domain_scores_codex":[0.9971992,0.0004172424,0.0007776365,0.0009074233,0.0001823756,0.0005160749],"domain_scores_gemma":[0.9982835,0.0001673388,0.0003665786,0.0007522598,0.0001148131,0.0003154952],"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":[0.00005273091,0.0001522294,0.001175108,0.0002012995,0.00002892402,0.0002319972,0.0003435985,0.0008318751,0.9919556,0.000004259943,0.00177124,0.003251151],"study_design_scores_gemma":[0.0008190667,0.0001650659,0.0004704923,0.0000186884,0.00005484417,0.00009422511,0.0002089186,0.001065769,0.8843426,0.0002679894,0.1119715,0.0005208572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9412469,0.01249049,0.039798,0.0001033878,0.002074535,0.0005667411,0.00001291203,0.0001825681,0.003524452],"genre_scores_gemma":[0.8766479,0.002885572,0.1000958,0.0004234657,0.0002329868,0.00002045037,0.0003946269,0.0001243885,0.01917474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1102002,"threshold_uncertainty_score":0.9998631,"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."}}