Contact Information:
Kun Liang
Research interests
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Large-scale inference
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Statistical genetics
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High-dimensional statistics
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Machine learning
Education/biography
- PhD, Iowa State University, U.S.A.
- BE, TsingHua University, China
Selected publications
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MacDonald, P.*, Liang, K., and Janssen, A. (2019), “Dynamic adaptive procedures that control the false discovery rate,” Electronic Journal of Statistics, 13, 3009–3024.
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Liang, K., Du, C., You, H.*, and Nettleton, D. (2018), “A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets,” BMC bioinformatics, 19.
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Nie, Z.*, and Liang, K. (2017), “Adaptive filtering increases power to detect differentially expressed genes,” in New advances in statistics and data science, Springer, pp. 127–136.
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Liang, K. (2016), “False discovery rate estimation for large-scale homogeneous discrete p-values,” Biometrics, 72, 639–648.
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Liang, K., and Keleş, S. (2012), “Detecting differential binding of transcription factors with ChIP-seq,” Bioinformatics, 28, 121–122.
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Liang, K., and Keleş, S. (2012), “Normalization of ChIP-seq data with control,” BMC Bioinformatics, 13, 199.
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Liang, K., and Nettleton, D. (2012), “Adaptive and dynamic adaptive procedures for false discovery rate control and estimation,” Journal of the Royal Statistical Society, Series B, 74, 163–182.
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Liang, K., and Nettleton, D. (2010), “A hidden Markov model approach to testing multiple hypotheses on a tree-transformed Gene Ontology graph,” Journal of the American Statistical Association, 105, 1444–1454.