{"id":"W2918882492","doi":"10.1093/bioinformatics/btz139","title":"SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Data mining; Rank (graph theory); Similarity (geometry); Spectral clustering; Representation (politics); Noise (video); Embedding; Artificial intelligence; Machine learning; Mathematics; Image (mathematics)","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":[0.0001378112,0.0001253553,0.0001282353,0.00003204972,0.00006697418,0.00004513564,0.00007004748,0.0001395764,0.000003858846],"category_scores_gemma":[0.00004010474,0.0001214119,0.00004848566,0.00007650213,0.00002153522,0.00001636268,0.00003387897,0.00005789994,0.000008037977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001379123,"about_ca_system_score_gemma":0.00002162123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004326015,"about_ca_topic_score_gemma":0.00004423271,"domain_scores_codex":[0.9993835,0.00001779064,0.0002065497,0.000155052,0.00007537829,0.0001617357],"domain_scores_gemma":[0.9995452,0.00003382389,0.0001060401,0.0001727494,0.00009291449,0.00004931211],"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":[0.0002462783,0.00003202587,0.0004400063,0.000264354,0.00002695957,9.14264e-8,0.0005519541,0.0009179696,0.989801,0.000001786893,0.0006570629,0.00706051],"study_design_scores_gemma":[0.001136563,0.0004080715,0.0001208103,0.0000146509,0.00002187203,0.000004107315,0.0004439345,0.3121763,0.683701,0.00001013804,0.001788543,0.0001739876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4483388,0.00005936408,0.5501735,0.00002406421,0.0002453673,0.0004234959,0.0000233003,0.00001169689,0.0007004199],"genre_scores_gemma":[0.9002566,0.0001166455,0.09829222,0.00019685,0.00008653301,0.00001756818,0.0001693252,0.00002674046,0.0008374573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4519178,"threshold_uncertainty_score":0.4951032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253753116997879,"score_gpt":0.2482468645510999,"score_spread":0.2357093333811211,"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."}}