{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304676,0.0007309741,0.001304874,0.001617779,0.000286681,0.0006617857,0.001353862,0.0008394111,0.001588488],"category_scores_gemma":[0.001812355,0.0003124374,0.001181095,0.001464298,0.0005055561,0.001146287,0.001440145,0.0008357852,0.001186453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003337872,"about_ca_system_score_gemma":0.0005075096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135788,"about_ca_topic_score_gemma":0.001725431,"domain_scores_codex":[0.9992797,0.0001477893,0.00003591034,0.0001847248,0.000270578,0.00008144191],"domain_scores_gemma":[0.9991519,0.0002036354,0.0001102395,0.0001716084,0.0002940782,0.00006840291],"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":[0.0004427477,0.0002875312,0.004714082,0.0002138512,0.0002092406,0.0002426206,0.0001916195,0.1131175,0.180261,0.005756454,0.006849391,0.6877138],"study_design_scores_gemma":[0.00001960618,0.0000761155,0.002089093,0.000005950136,0.0000213035,0.000205871,0.00003910788,0.9533158,0.03909519,0.003093647,0.002005783,0.00003249168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01773758,0.0001523791,0.9799605,0.00009420691,0.00003277263,0.00004605807,0.0001733687,0.001444584,0.0003585664],"genre_scores_gemma":[0.1930279,0.0002578248,0.8016513,0.0001932448,0.00006147375,0.0001508881,0.001509003,0.0001752761,0.002973107],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001617779,"threshold_uncertainty_score":0.006899834,"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."}}