References in zbMATH (referenced in 31 articles )

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  1. Qiu, Yumou; Chen, Song Xi; Nettleton, Dan: Detecting rare and faint signals via thresholding maximum likelihood estimators (2018)
  2. Bécu, Jean-Michel; Grandvalet, Yves; Ambroise, Christophe; Dalmasso, Cyril: Beyond support in two-stage variable selection (2017)
  3. Fu, Rong; Wang, Pei; Ma, Weiping; Taguchi, Ayumu; Wong, Chee-Hong; Zhang, Qing; Gazdar, Adi; Hanash, Samir M.; Zhou, Qinghua; Zhong, Hua; Feng, Ziding: A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data (2017)
  4. Lun, Aaron T.L.; Smyth, Gordon K.: No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data (2017)
  5. Shang, Kan; Reilly, Cavan: Non parametric Bayesian analysis of the two-sample problem with censoring (2017)
  6. Bonafede, Elisabetta; Picard, Franck; Robin, Stéphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  7. Cui, Shiqi; Ji, Tieming; Li, Jilong; Cheng, Jianlin; Qiu, Jing: What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment (2016)
  8. Han, Sung Won; Zhong, Hua: Estimation of sparse directed acyclic graphs for multivariate counts data (2016)
  9. Kruppa, Jochen; Kramer, Frank; Beißbarth, Tim; Jung, Klaus: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments (2016)
  10. Li, Chung-I; Shyr, Yu: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (2016)
  11. Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data (2016)
  12. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  13. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  14. Gelfond, Jonathan A.; Ibrahim, Joseph G.; Chen, Ming-Hui; Sun, Wei; Lewis, Kaitlyn; Kinahan, Sean; Hibbs, Matthew; Buffenstein, Rochelle: Homology cluster differential expression analysis for interspecies mRNA-seq experiments (2015)
  15. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  16. Liu, Fangfang; Wang, Chong; Liu, Peng: A semi-parametric Bayesian approach for differential expression analysis of RNA-seq data (2015)
  17. Nguyen, Yet; Nettleton, Dan; Liu, Haibo; Tuggle, Christopher K.: Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates (2015)
  18. Niu, Liang; Lin, Shili: A Bayesian mixture model for chromatin interaction data (2015)
  19. Thorne, Thomas: Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data (2015)
  20. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)

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