{"id":"W4408956502","doi":"10.1093/bioinformatics/btaf137","title":"scMUSCL: multi-source transfer learning for clustering scRNA-seq data","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transfer of learning; Cluster analysis; Computer science; Transfer (computing); Data source; Artificial intelligence; Data mining; Parallel computing","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":[0.002167681,0.001996978,0.0009766686,0.001791703,0.001079289,0.001219367,0.004190905,0.001883637,0.006513853],"category_scores_gemma":[0.006642856,0.0006955588,0.001459022,0.001873603,0.0011433,0.001819663,0.003351495,0.002588597,0.004670861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545451,"about_ca_system_score_gemma":0.002513527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007647596,"about_ca_topic_score_gemma":0.012415,"domain_scores_codex":[0.9989575,0.0001914273,0.00005231265,0.0003914367,0.0003154628,0.00009184721],"domain_scores_gemma":[0.9982999,0.0005419473,0.0001318111,0.0004817962,0.0004171806,0.0001273021],"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":[0.0007704751,0.0004063521,0.005978201,0.0009746911,0.0004896984,0.0005722496,0.0004363484,0.3323194,0.05270707,0.008248828,0.111231,0.4858657],"study_design_scores_gemma":[0.00005197502,0.00008340208,0.0009182045,0.00003024353,0.00001895285,0.0001127369,0.0000612628,0.9630184,0.0171141,0.01149203,0.007046637,0.00005200487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01743056,0.0004740398,0.9053926,0.0005325236,0.0002215285,0.0002466954,0.004217603,0.06961386,0.001870627],"genre_scores_gemma":[0.1962078,0.0004866767,0.7599257,0.001241072,0.0001468486,0.001241636,0.02795263,0.00595169,0.006845947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007647596,"threshold_uncertainty_score":0.02179104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04198762505065439,"score_gpt":0.2798412973435241,"score_spread":0.2378536722928697,"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."}}