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), 鈥淒ynamic adaptive procedures that control the false discovery rate,鈥澛Electronic Journal of Statistics, 13, 3009鈥3024.
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Liang, K., Du, C., You, H.*, and Nettleton, D. (2018), 鈥淎 hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets,鈥澛BMC bioinformatics, 19.
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Nie, Z.*, and Liang, K. (2017), 鈥淎daptive filtering increases power to detect differentially expressed genes,鈥 in聽New advances in statistics and data science, Springer, pp. 127鈥136.
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Liang, K. (2016), 鈥淔alse discovery rate estimation for large-scale homogeneous discrete p-values,鈥澛Biometrics, 72, 639鈥648.
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Liang, K., and Kele艧, S. (2012), 鈥淒etecting differential binding of transcription factors with ChIP-seq,鈥澛Bioinformatics, 28, 121鈥122.
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Liang, K., and Kele艧, S. (2012), 鈥淣ormalization of ChIP-seq data with control,鈥澛BMC Bioinformatics, 13, 199.
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Liang, K., and Nettleton, D. (2012), 鈥淎daptive and dynamic adaptive procedures for false discovery rate control and estimation,鈥澛Journal of the Royal Statistical Society, Series B, 74, 163鈥182.
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Liang, K., and Nettleton, D. (2010), 鈥淎 hidden Markov model approach to testing multiple hypotheses on a tree-transformed Gene Ontology graph,鈥澛Journal of the American Statistical Association, 105, 1444鈥1454.