My research interests lie in the field of Computational Biology and Bioinformatics, which involves developing statistical and computational methods to analyze large-scale biological data and applying these methods to study biological and biomedical problems. Specifically, I am interested in large-scale analysis of protein interactions, signal transduction pathways, genomic sequences, and gene expression.
My research primarily focuses on the area of systems biology, which involves the development of sound and comprehensive statistical models to integrate information from diverse sources in order to reconstruct biological networks, such as protein interaction networks. In the area of genome-wide protein interaction prediction, the high-throughput techniques such as yeast two-hybrid screening methods used for systematically identifying protein interactions suffer from high false positive rates and high false negative rates due to the limitation of these techniques. I have been working on developing statistical methods to integrate large-scale protein interaction data from diverse organisms in order to improve the reliability of protein interactions inference. Another topic in the area of systems biology I am interested in is signal transduction pathway reconstruction. I have developed an approach that integrates protein-protein interaction data and gene expression data from microarray chips for predicting the order of signaling pathway components, assuming all the components on the pathways are known. My current research on this topic concentrates on the incorporation of other types of information such as protein phosphorylation data, and the development of more elaborate statistical approaches to make further prediction and modeling of the signal transduction networks.
From a statistical point of view, I am interested in the field of Bayesian inference and its applications in Bioinformatics. Bayesian inference has been widely used in the analysis of high throughput bioinformatics data because biological evidence can be flexibly incorporated into Bayesian models and it naturally lends itself to efficient computational methods. Currently, I am working on the development of a Bayesian approach coupled with Markov Chain Monte Carlo (MCMC) to inferring protein complexes and functional modules using high-throughput mass spectrometry data, with considering the topological structures of the protein interaction networks when making the inference.
Zhang, Z, Harrison, P, Liu Y, Gerstein, M. (2003) Millions of years of evolution preserved: a comprehensive catalogue of over 8000 pseudogenes in the human genome. Genome Research, 13:2541-2558.
Liu, Y, Zhao, H. (2004) A computational approach for ordering signal transduction pathway components from genomics and proteomics data. BMC Bioinformatics, 5, 158.
Liu, Y, Liu, N, and Zhao, H. (2005) Inferring protein-protein interactions through high-throughput interaction data from diverse organisms. Bioinformatics, 21: 3279-3285.
Liu, Y, Sun, N, Liu, J, Chen, L, McIntosh, M, Zheng, L, and Zhao, H. (2007) Identifying stage-specific genes by combining information from two different types of oligonucleotide arrays. Methods of Microarray Data Analysis V, 59-75.
Kim, I., Liu, Y., and Zhao, H. Bayesian methods for predicting interacting protein pairs using domain information. Biometrics (2007), 63:824-833.
Liu, Y., Kim, I., and Zhao, H. Protein interaction predictions from diverse sources. Drug Discovery Today (2008), 13: 409-416.
Redell, J., Liu, Y. and Dash, P. Traumatic Brain Injury Alters Expression of Hippocampal MicroRNAs: Potential Regulators of Multiple Pathophysiological Processes. Journal of Neuroscience Research (2009), 87: 1435-1448.
Liu, Y., Kim, I., and Zhao, H. Protein interaction predictions from diverse sources. In: New Developments in Biostatistics and Bioinformatics (Fan, J., Lin, X., Liu, J., eds.), Higher Education Press & World Scientific, 159-175, 2009.
Kim, I., Liu, Y., and Zhao, H. Sparsity priors for protein-protein interaction predictions. In: Bayesian Modeling in Bioinformatics (Dey, D., Ghosh S., and Mallick, B., eds.), Chapman & Hall, 255-270, 2010.
Tan, Y., and Liu, Y. Evaluation of statistical methods for identifying genes differentially expressed among multiple groups, Bioinformation, in press.
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