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Yulan Liang, Ph.D.

Affiliations: 
2003 The University of Memphis, Memphis, TN, United States 
Area:
Statistics, Biostatistics Biology, Computer Science
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"Yulan Liang"
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Parents

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Ebenezer O. George grad student 2003 The University of Memphis
 (Gene expression temporal patterns classification with hierarchical Bayesian neural networks and time lagged recurrent neural networks.)

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Publications

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Liang Y, Kelemen A, Kelemen A. (2019) Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies. Statistical Applications in Genetics and Molecular Biology. 18
Liang Y, Kelemen A. (2017) Computational dynamic approaches for temporal omics data with applications to systems medicine. Biodata Mining. 10: 20
Liang Y, Kelemen A. (2017) Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications. Briefings in Bioinformatics
Liang Y, Kelemen A. (2016) Bayesian state space models for dynamic genetic network construction across multiple tissues. Statistical Applications in Genetics and Molecular Biology
Liang Y, Kelemen A. (2011) Sequential Support Vector Regression with Embedded Entropy for SNP Selection and Disease Classification. Statistical Analysis and Data Mining. 4: 301-312
Liang Y, Kelemen A. (2009) Bayesian finite Markov mixture model for temporal multi-tissue polygenic patterns. Biometrical Journal. Biometrische Zeitschrift. 51: 56-69
Liang Y, Kelemen A. (2008) Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration. Bmc Bioinformatics. 9: 354
Liang Y, Kelemen A. (2008) Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases Statistics Surveys. 2: 43-60
Liang Y, Kelemen A. (2007) Bayesian state space models for inferring and predicting temporal gene expression profiles. Biometrical Journal. Biometrische Zeitschrift. 49: 801-14
Liang Y, Kelemen A, Tayo B. (2007) Model-based or algorithm-based? Statistical evidence for diabetes and treatments using gene expression. Statistical Methods in Medical Research. 16: 139-53
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