{"id":"W3134128419","doi":"10.1016/j.gpb.2020.09.004","title":"SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement","year":2021,"lang":"en","type":"article","venue":"Genomics Proteomics & Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Central South University","keywords":"Cluster analysis; Computer science; Pairwise comparison; Similarity (geometry); Representation (politics); Artificial intelligence; Subspace topology; Sparse approximation; Pattern recognition (psychology); Visualization; Biclustering; Data mining; Identification (biology); Correlation clustering; CURE data clustering algorithm; Image (mathematics); Biology","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":[0.0002205579,0.0002242094,0.0001839874,0.00007345874,0.0001663962,0.000103157,0.0001257052,0.0002263692,0.00001133326],"category_scores_gemma":[0.00007580489,0.0002446151,0.00007999287,0.000180756,0.00006099217,0.00001331021,0.00008190731,0.0001815333,0.00001969625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007032177,"about_ca_system_score_gemma":0.0001896656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001345535,"about_ca_topic_score_gemma":0.00005225759,"domain_scores_codex":[0.998824,0.00004820583,0.0003747525,0.0003150598,0.0001591013,0.0002788696],"domain_scores_gemma":[0.9990361,0.00001640965,0.0001725531,0.0004851452,0.0001786752,0.0001110926],"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":[0.0002784014,0.0001297669,0.0005025118,0.0001006556,0.00002329395,0.000003150065,0.000198985,0.001818364,0.9926615,0.00002521335,0.0001182219,0.004139962],"study_design_scores_gemma":[0.0009155202,0.0003770241,0.0001592269,0.00001202479,0.00003091141,0.00001071786,0.0001462962,0.06612629,0.9277551,0.00007083519,0.004121188,0.0002748712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8636335,0.0001331185,0.1339414,0.0001490085,0.0004057686,0.0005011086,0.00002970048,0.00001998209,0.001186458],"genre_scores_gemma":[0.9314367,0.0006029634,0.06615885,0.0009201454,0.0001435988,0.0000249408,0.0003655551,0.00003623893,0.0003109637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0678033,"threshold_uncertainty_score":0.9975114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01532651456170428,"score_gpt":0.2294066545997239,"score_spread":0.2140801400380196,"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."}}