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Journal of the American Statistical Association Volume 105, 2010 - Issue 492
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Theory and Methods

A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph

Kun Liang Kun Liang is Doctoral Candidate, Department of Statistics, Iowa State University, Ames, IA 50011 . Dan Nettleton is Professor, Department of Statistics, Iowa State University, Ames, IA 50011 . This work was supported by the National Science Foundation grant No. 0714978 and CCF-0811804, and National Research Initiative of the USDA-CSREES grant No. 2008-35600-18786. The authors wish to thank the editor, the associate editor, and two reviewers for their comments and suggestions which led to an improved manuscript.
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Dan Nettleton Kun Liang is Doctoral Candidate, Department of Statistics, Iowa State University, Ames, IA 50011 . Dan Nettleton is Professor, Department of Statistics, Iowa State University, Ames, IA 50011 . This work was supported by the National Science Foundation grant No. 0714978 and CCF-0811804, and National Research Initiative of the USDA-CSREES grant No. 2008-35600-18786. The authors wish to thank the editor, the associate editor, and two reviewers for their comments and suggestions which led to an improved manuscript.
Pages 1444-1454 | Received 01 Apr 2010, Published online: 01 Jan 2012

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Xiaojuan Hao, Kent M. Eskridge & Dong Wang. (2022) Variational Bayesian inference for association over phylogenetic trees for microorganisms. Journal of Applied Statistics 49:5, pages 1140-1153.
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Articles from other publishers (16)

Joungyoun Kim, Johan Lim & Jong Soo Lee. (2022) Semi-parametric hidden Markov model for large-scale multiple testing under dependency. Statistical Modelling, pages 1471082X2211212.
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Liuhua Peng, Long Qu & Dan Nettleton. (2021) Variable importance assessments and backward variable selection for multi-sample problems. Journal of Multivariate Analysis 186, pages 104807.
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Tingting Cui, Pengfei Wang & Wensheng Zhu. (2021) Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models. TEST 30:3, pages 737-757.
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Xia Wang, Ali Shojaie & Jian Zou. (2019) Bayesian hidden Markov models for dependent large-scale multiple testing. Computational Statistics & Data Analysis 136, pages 123-136.
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Kun Liang, Chuanlong Du, Hankun You & Dan Nettleton. (2018) A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets. BMC Bioinformatics 19:1.
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T. Tony CaiWenguang Sun. (2017) Large-Scale Global and Simultaneous Inference: Estimation and Testing in Very High Dimensions. Annual Review of Economics 9:1, pages 411-439.
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Ting Wang, Jiancang Zhuang, Kazushige Obara & Hiroshi Tsuruoka. (2017) Hidden Markov modelling of sparse time series from non-volcanic tremor observations. Journal of the Royal Statistical Society: Series C (Applied Statistics) 66:4, pages 691-715.
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Zixin Nie & Kun Liang. 2017. New Advances in Statistics and Data Science. New Advances in Statistics and Data Science 127 136 .
Rosa J. Meijer & Jelle J. Goeman. (2016) Multiple Testing of Gene Sets from Gene Ontology: Possibilities and Pitfalls. Briefings in Bioinformatics 17:5, pages 808-818.
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Kun Liang. (2016) False discovery rate estimation for large-scale homogeneous discrete p -values . Biometrics 72:2, pages 639-648.
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Sam Benidt & Dan Nettleton. (2015) SimSeq: a nonparametric approach to simulation of RNA-sequence datasets. Bioinformatics 31:13, pages 2131-2140.
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Wenguang Sun & Zhi Wei. (2015) Hierarchical recognition of sparse patterns in large-scale simultaneous inference. Biometrika 102:2, pages 267-280.
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Rosa J. Meijer & Jelle J. Goeman. (2015) A multiple testing method for hypotheses structured in a directed acyclic graph. Biometrical Journal 57:1, pages 123-143.
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Garrett Saunders, John R Stevens & S Clay Isom. (2014) A shortcut for multiple testing on the directed acyclic graph of gene ontology. BMC Bioinformatics 15:1.
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Long Qu, Dan Nettleton & Jack C. M. Dekkers. (2012) A Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data. Biometrics 68:4, pages 1168-1177.
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Daniel J. Schaid, Jason P. Sinnwell, Gregory D. Jenkins, Shannon K. McDonnell, James N. Ingle, Michiaki Kubo, Paul E. Goss, Joseph P. Costantino, D. Lawrence Wickerham & Richard M. Weinshilboum. (2011) Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies. Genetic Epidemiology 36:1, pages 3-16.
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