{"id":"W2950983802","doi":"10.1093/bioinformatics/btz095","title":"Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Institute of General Medical Sciences; National Institutes of Health; Microsoft Research","keywords":"Autoencoder; Computer science; Computational biology; Dimensionality reduction; Feature (linguistics); Single cell sequencing; Biology; Genomics; Gene; Artificial intelligence; Mutation; Genetics; Genome; Exome sequencing; Deep learning","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":[0.00116509,0.0009605748,0.0007020593,0.000528976,0.0003398426,0.0007648246,0.001500841,0.001232888,0.002399037],"category_scores_gemma":[0.003998498,0.0007201678,0.0009367883,0.0004768887,0.0006252436,0.0007372765,0.001390118,0.002438477,0.001182294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006724487,"about_ca_system_score_gemma":0.001264479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008673755,"about_ca_topic_score_gemma":0.0134136,"domain_scores_codex":[0.9996722,0.000107176,0.00001828005,0.0001107309,0.00006376824,0.00002788608],"domain_scores_gemma":[0.9991141,0.0005990363,0.00004254539,0.00009241994,0.0001145153,0.00003745584],"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":[0.0002355777,0.00007040898,0.002070068,0.0003561648,0.0002884651,0.0001692403,0.0001498778,0.7500218,0.01950861,0.009914444,0.01221999,0.2049954],"study_design_scores_gemma":[0.00001005565,0.00001312445,0.0002138788,0.000009340791,0.000008689139,0.00002341633,0.000007432313,0.9922387,0.001825838,0.004272451,0.001368011,0.000009067187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009116348,0.0005798133,0.98635,0.0003229657,0.00007439468,0.00006382081,0.0008040194,0.002063649,0.0006250525],"genre_scores_gemma":[0.2428338,0.0006339029,0.7423587,0.0006194814,0.00008068138,0.0005304305,0.005932787,0.0009143126,0.006095911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008673755,"threshold_uncertainty_score":0.01724654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147508797227019,"score_gpt":0.2393536263880751,"score_spread":0.2078785384158049,"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."}}