{"id":"W3015931701","doi":"10.1101/2020.04.08.028779","title":"SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; Higher Education Discipline Innovation Project; Fundamental Research Funds for the Central Universities; Central South University; National Natural Science Foundation of China","keywords":"Cluster analysis; Pairwise comparison; Similarity (geometry); Computer science; Subspace topology; Representation (politics); Artificial intelligence; Pattern recognition (psychology); Identification (biology); Visualization; Data mining; Biclustering; Machine learning; Correlation clustering; Image (mathematics); CURE data clustering algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002071641,0.000385995,0.0002895679,0.00007697244,0.0001191561,0.0001075335,0.0002098963,0.0004938687,0.00001104351],"category_scores_gemma":[0.0001230425,0.0004405785,0.0001031638,0.0001875218,0.00006976693,0.000006230581,0.0001550075,0.0004237764,0.0000147636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006866927,"about_ca_system_score_gemma":0.0002030327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005464081,"about_ca_topic_score_gemma":0.0000112217,"domain_scores_codex":[0.9980551,0.0001258309,0.0002857686,0.001012277,0.0002334762,0.0002875467],"domain_scores_gemma":[0.9986137,0.00001771251,0.0002085384,0.0007416949,0.0002262359,0.0001921037],"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":[0.00027997,0.0001428772,0.0024891,0.0001611395,0.00004085829,0.00001034989,0.00000467927,0.0002978198,0.99637,0.000006332313,0.00019077,0.000006154561],"study_design_scores_gemma":[0.0006100422,0.0003538246,0.00757736,0.00004553079,0.00007560597,7.186739e-9,0.000001909725,0.00382084,0.9845656,0.000001042595,0.002498301,0.0004499216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977764,0.0005525443,0.01963382,0.0002599857,0.001068145,0.0005567829,0.00006236212,0.00007796846,0.00002444254],"genre_scores_gemma":[0.9964669,0.0003848381,0.002079139,0.0005229145,0.0004052543,0.00005254575,0.000004372154,0.00007637094,0.000007686983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01870293,"threshold_uncertainty_score":0.9998046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212761922103963,"score_gpt":0.2267044360323723,"score_spread":0.205428243821976,"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."}}