{"id":"W4412070457","doi":"10.1093/bib/bbaf244","title":"scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data","year":2025,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Science Foundation of Tianjin City","keywords":"Cluster analysis; Benchmarking; Computer science; Biclustering; Data mining; Representation (politics); Graph; Computational biology; Biology; Machine learning; Correlation clustering; Theoretical computer science; CURE data clustering algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004326636,0.0002483692,0.0002770029,0.0001462655,0.0001203912,0.0001460551,0.000551841,0.0002554827,0.000003776299],"category_scores_gemma":[0.0001292617,0.0002448603,0.0000588025,0.000210975,0.00009691973,0.00004072866,0.000204182,0.0001531104,9.00267e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002105667,"about_ca_system_score_gemma":0.0001633881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009520132,"about_ca_topic_score_gemma":0.0007717679,"domain_scores_codex":[0.9986274,0.00004145896,0.0005245575,0.0003913937,0.0001018031,0.0003134294],"domain_scores_gemma":[0.998884,0.00007013277,0.0001345226,0.0007361526,0.00008679208,0.00008835318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002091527,0.0009857322,0.01325407,0.002263057,0.0003158687,0.000006981395,0.001523611,0.002474798,0.6942232,0.0003717781,0.02108715,0.2614022],"study_design_scores_gemma":[0.004470177,0.000316457,0.00162479,0.0002748811,0.0001051729,0.000004037378,0.0004416423,0.7817576,0.1672605,0.000552002,0.0425368,0.0006559425],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1719274,0.000455004,0.825306,0.0003208269,0.0002521939,0.0004949544,0.0007530143,0.00003837939,0.0004522369],"genre_scores_gemma":[0.1525893,0.000239921,0.8360083,0.005180713,0.0001026176,0.0000406824,0.005635961,0.00004657421,0.0001559512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7792828,"threshold_uncertainty_score":0.9985115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515994668713058,"score_gpt":0.293656779255029,"score_spread":0.2684968325678985,"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."}}