{"id":"W3003746841","doi":"10.1101/2020.01.29.926022","title":"CopyMix: Mixture Model Based Single-Cell Clustering and Copy Number Profiling using Variational Inference","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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency; Western University","funders":"","keywords":"Cluster analysis; Profiling (computer programming); Inference; Computer science; Copy-number variation; Computational biology; Copy number analysis; Single cell sequencing; Data mining; Biology; Artificial intelligence; Phenotype; Gene; Genetics; Exome sequencing; 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":[0.003390645,0.001105697,0.001206702,0.0009900937,0.000696605,0.001593818,0.0030883,0.001919443,0.002982606],"category_scores_gemma":[0.006569538,0.001279949,0.001751667,0.0008448992,0.001443278,0.001372505,0.002367278,0.002612649,0.0007756071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001692385,"about_ca_system_score_gemma":0.001875046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074942,"about_ca_topic_score_gemma":0.01067351,"domain_scores_codex":[0.999016,0.0004254254,0.00002898703,0.0002644448,0.0002055489,0.00005952666],"domain_scores_gemma":[0.9978887,0.001511007,0.000127211,0.000209074,0.0001617017,0.000102365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000178939,0.00006183325,0.002457865,0.000112764,0.0002477369,0.00009996846,0.0001400051,0.9007461,0.007391045,0.02890223,0.002688424,0.0569731],"study_design_scores_gemma":[0.000005313786,0.000005490553,0.00007904303,0.000003163163,0.000004082638,0.00001031412,0.000003317199,0.9933679,0.0008293611,0.005271209,0.0004142985,0.000006575519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006247031,0.00009218479,0.9922183,0.0001517159,0.0000221477,0.00003477629,0.0001189207,0.0007321389,0.0003828348],"genre_scores_gemma":[0.2163033,0.0001922456,0.7758396,0.0002796176,0.00009314638,0.0002816453,0.001305515,0.001138528,0.004566393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01074942,"threshold_uncertainty_score":0.02137375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047843220421992,"score_gpt":0.2388881015343103,"score_spread":0.2184096693300904,"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."}}