{"id":"W4386709519","doi":"10.1016/j.crmeth.2023.100581","title":"Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics","year":2023,"lang":"en","type":"article","venue":"Cell Reports Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canada First Research Excellence Fund; University of Toronto","keywords":"Autoencoder; Dynamics (music); Inference; RNA; RNA splicing; Gene; Computer science; Biology; Computational biology; Genetics; Artificial intelligence; Deep learning; Physics","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":[0.000433067,0.0004421692,0.0004454759,0.0004037226,0.0002069319,0.0008251267,0.0007224572,0.0005944069,0.001244259],"category_scores_gemma":[0.002001662,0.0005132877,0.0006578708,0.0004620637,0.0005892824,0.00107423,0.0006365626,0.001266018,0.000408472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000876889,"about_ca_system_score_gemma":0.001053236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005873363,"about_ca_topic_score_gemma":0.01002407,"domain_scores_codex":[0.9998598,0.00002032776,0.000005254686,0.00006480017,0.00002897277,0.00002087239],"domain_scores_gemma":[0.9994345,0.0003117565,0.00008059313,0.00007833294,0.00005283859,0.00004206177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001166438,0.00005401482,0.006636167,0.0000805873,0.00005644199,0.000107745,0.0001035416,0.8918332,0.02969752,0.0249034,0.001432937,0.04497773],"study_design_scores_gemma":[0.000001874753,0.000003182276,0.0002928506,0.000001933424,0.000002153301,0.000006553987,0.000005112123,0.9926072,0.001484878,0.005370388,0.0002197317,0.000004062507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0617489,0.00012462,0.9361696,0.0001439911,0.0000246567,0.00001372157,0.0004978285,0.0007484928,0.0005280933],"genre_scores_gemma":[0.7847227,0.0003819262,0.2084787,0.0001431329,0.00003793599,0.0000976589,0.002065223,0.000345058,0.003727718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005873363,"threshold_uncertainty_score":0.01167834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01983450742603918,"score_gpt":0.2702643652185139,"score_spread":0.2504298577924747,"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."}}