{"id":"W4283012806","doi":"10.1093/bib/bbac223","title":"DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data","year":2022,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Institute of Dental and Craniofacial Research; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Simons Foundation; National Institutes of Health; National Science Foundation","keywords":"Imputation (statistics); Computer science; Robustness (evolution); Deconvolution; Benchmarking; Curse of dimensionality; Data mining; Cluster analysis; Dimensionality reduction; Computational biology; Embedding; Artificial intelligence; Machine learning; Algorithm; Missing data; Biology; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008365484,0.001550695,0.001659924,0.001666009,0.001001098,0.001992212,0.003397883,0.00174969,0.003066269],"category_scores_gemma":[0.01672826,0.0009198604,0.002325966,0.001874821,0.001361775,0.001782232,0.002936166,0.004808993,0.00305923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111289,"about_ca_system_score_gemma":0.002944867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00311727,"about_ca_topic_score_gemma":0.00540721,"domain_scores_codex":[0.9967733,0.001181313,0.0001614023,0.0007082103,0.001004167,0.0001715597],"domain_scores_gemma":[0.9941999,0.002916539,0.000466038,0.001335114,0.0008857977,0.000196657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008180089,0.0002989846,0.006182248,0.0007719361,0.001152964,0.0004801247,0.0005480823,0.3002116,0.06879065,0.0418236,0.02538679,0.5535351],"study_design_scores_gemma":[0.00003502757,0.00007885752,0.001011773,0.0000286993,0.0000434625,0.0001528088,0.00004264601,0.9371872,0.02562745,0.02406386,0.01164263,0.00008553831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002341942,0.0001449798,0.9934948,0.000102049,0.00003874179,0.00002381923,0.0003407564,0.003290856,0.0002221714],"genre_scores_gemma":[0.05133512,0.0003740951,0.9409066,0.0002451346,0.0000975991,0.0003123946,0.00315937,0.001517994,0.002051718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008365484,"threshold_uncertainty_score":0.04424143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03287022522796098,"score_gpt":0.2765213581485351,"score_spread":0.2436511329205741,"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."}}