{"id":"W3207337285","doi":"10.1093/bib/bbab413","title":"sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Canadian Cancer Society Research Institute","keywords":"Copy-number variation; Biology; Transcriptome; Computational biology; Gene dosage; Gene; Genetics; Gene expression profiling; Genome; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001897587,0.001035288,0.001352561,0.002093564,0.001024351,0.00158187,0.001406042,0.001304985,0.003209742],"category_scores_gemma":[0.002829154,0.0007873091,0.001191538,0.00174224,0.0005562145,0.0006972502,0.001752828,0.001685053,0.00145549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016174,"about_ca_system_score_gemma":0.001038144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002271852,"about_ca_topic_score_gemma":0.005865975,"domain_scores_codex":[0.9982353,0.0001834539,0.00009298152,0.0005997436,0.000765376,0.0001230447],"domain_scores_gemma":[0.9982582,0.0006746649,0.0003047485,0.0003514669,0.0002664393,0.0001445454],"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":[0.0007966599,0.0001401333,0.02051621,0.001296984,0.0006789538,0.0007231251,0.000604859,0.009172697,0.8484524,0.00422949,0.02496251,0.08842593],"study_design_scores_gemma":[0.0001779622,0.0005238274,0.07937477,0.0001576957,0.000344347,0.002194871,0.0002555255,0.1495995,0.6772576,0.008427641,0.0812935,0.0003927652],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3049778,0.003852382,0.5225879,0.0006903191,0.0005881463,0.0007928417,0.09536488,0.06108524,0.0100605],"genre_scores_gemma":[0.3525439,0.001759504,0.5053235,0.001266806,0.0001975608,0.002295913,0.1238558,0.007381833,0.005375202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003209742,"threshold_uncertainty_score":0.01073766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114547254914039,"score_gpt":0.2209572802861319,"score_spread":0.2098118077369915,"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."}}