Correction: Visualizing the structure of RNA-seq expression data using grade of membership models
نویسندگان
چکیده
منابع مشابه
Correction: Visualizing the structure of RNA-seq expression data using grade of membership models
[This corrects the article DOI: 10.1371/journal.pgen.1006599.].
متن کاملVisualizing the structure of RNA-seq expression data using grade of membership models
Grade of membership models, also known as "admixture models", "topic models" or "Latent Dirichlet Allocation", are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from multiple "populations", and in natural language processing to model documents h...
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Grade of membership models, also known as “admixture models”, “topic models” or “Latent Dirichlet Allocation”, are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from multiple “populations”, and in natural language processing to model documents h...
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Grade of membership or GoM models (also known as admixture models or Latent Dirichlet Allocation”) are a generalization of cluster models that allow each sample to have membership in multiple clusters. It is widely used to model ancestry of individuals in population genetics based on SNP/ microsatellite data and also in natural language processing for modeling documents [1, 3]. This R package i...
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data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...
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ژورنال
عنوان ژورنال: PLOS Genetics
سال: 2017
ISSN: 1553-7404
DOI: 10.1371/journal.pgen.1006759