{"id":"W3115322301","doi":"10.1101/2020.02.10.942607","title":"sciCNV: High-throughput paired profiling of transcriptomes and DNA copy number variations at single cell resolution","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Copy-number variation; Biology; Transcriptome; Computational biology; Gene dosage; Gene expression profiling; Gene; Copy number analysis; Genetics; Gene expression; Genome","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":[0.001963322,0.0008334678,0.001076994,0.001886652,0.000973733,0.001752316,0.001272452,0.0009775297,0.005094523],"category_scores_gemma":[0.001875159,0.0006873056,0.0008417111,0.001398834,0.0006715805,0.0006504129,0.001533195,0.001616427,0.002642745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009616452,"about_ca_system_score_gemma":0.001138323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001449039,"about_ca_topic_score_gemma":0.003192442,"domain_scores_codex":[0.9981613,0.0002108865,0.00007807535,0.0004975272,0.0009391081,0.0001132206],"domain_scores_gemma":[0.9988877,0.0003512314,0.0001396071,0.0003162637,0.0002024541,0.0001028263],"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":[0.0006272138,0.0001043603,0.008808056,0.0007067526,0.0002701593,0.0003495434,0.0002455914,0.007303897,0.8530194,0.005152846,0.04979236,0.07361979],"study_design_scores_gemma":[0.0001110029,0.000186045,0.02981295,0.00007477286,0.000123478,0.00105905,0.0001236858,0.08671387,0.7751176,0.0109135,0.09555026,0.0002138254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2766008,0.003077329,0.5330883,0.001743977,0.001055258,0.0004674762,0.09495804,0.06835685,0.02065192],"genre_scores_gemma":[0.3684164,0.001560193,0.4595497,0.001319826,0.0003705471,0.001627633,0.1402857,0.01224806,0.014622],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005094523,"threshold_uncertainty_score":0.01704288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403799737588718,"score_gpt":0.2134475762499272,"score_spread":0.19940957887404,"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."}}