{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001128184,0.0007305869,0.001249411,0.001603665,0.00037254,0.0008161728,0.001456997,0.0008898095,0.001909242],"category_scores_gemma":[0.002019396,0.0003559662,0.001415573,0.001413158,0.0005193972,0.001316279,0.001611449,0.0009469345,0.001643932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003896873,"about_ca_system_score_gemma":0.0006670782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00193344,"about_ca_topic_score_gemma":0.002861048,"domain_scores_codex":[0.9992061,0.0001288531,0.00004655833,0.0002080539,0.0003143531,0.00009605513],"domain_scores_gemma":[0.9992616,0.0001786521,0.00009898741,0.0001470696,0.0002596724,0.00005406726],"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":[0.0003072051,0.000224142,0.004829938,0.0002863336,0.0002201492,0.0002100647,0.0002802462,0.07548743,0.1547356,0.006850906,0.009029817,0.7475381],"study_design_scores_gemma":[0.00002845459,0.0001248698,0.003411788,0.00001463092,0.00004112008,0.000459178,0.00008803241,0.93019,0.05285495,0.006337162,0.006384483,0.00006532998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009449841,0.0001666137,0.9878913,0.00006553542,0.0000329744,0.000055219,0.0002230791,0.001655907,0.0004595797],"genre_scores_gemma":[0.1488708,0.0003983889,0.8438002,0.0002414386,0.00007238256,0.0002668198,0.002488957,0.0002991154,0.003561906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00193344,"threshold_uncertainty_score":0.006387115,"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."}}