Browsing by Author "Liu, Lei"

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    A semiparametric recurrent events model with time-varying coefficients
    (Wiley Blackwell (John Wiley & Sons), 2013-03-15) Yu, Zhangsheng; Liu, Lei; Bravata, Dawn M.; Williams, Linda S.; Tepper, Robert S.; Department of Biostatistics, Richard M. Fairbanks School of Public Health
    We consider a recurrent events model with time-varying coefficients motivated by two clinical applications. We use a random effects (Gaussian frailty) model to describe the intensity of recurrent events. The model can accommodate both time-varying and time-constant coefficients. We use the penalized spline method to estimate the time-varying coefficients. We use Laplace approximation to evaluate the penalized likelihood without a closed form. We estimate the smoothing parameters in a similar way to variance components. We conduct simulations to evaluate the performance of the estimates for both time-varying and time-independent coefficients. We apply this method to analyze two data sets: a stroke study and a child wheeze study.
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    System modeling reveals the molecular mechanisms of HSC cell cycle alteration mediated by Maff and Egr3 under leukemia
    (BMC, 2017-10-03) Li, Rudong; Wang, Yin; Cheng, Hui; Liu, Gang; Cheng, Tao; Liu, Yunlong; Liu, Lei; Medical and Molecular Genetics, School of Medicine
    Background Molecular mechanisms of the functional alteration of hematopoietic stem cells (HSCs) in leukemic environment attract intensive research interests. As known in previous researches, Maff and Egr3 are two important genes having opposite functions on cell cycle; however, they are both highly expressed in HSCs under leukemia. Hence, exploring the molecular mechanisms of how the genes act on cell cycle will help revealing the functional alteration of HSCs. Results We herein utilize the bioinformatic resources to computationally model the acting mechanisms of Maff and Egr3 on cell cycle. Using the data of functional experiments as reference, molecular acting mechanisms are optimally enumerated through model selection. The results are consolidated by evidences from gene sequence analysis, thus having enhanced the confidence of our pilot findings, which suggest that HSCs possibly undergo a “adaptation - suppression” process in response to the malignant environment of leukemia. Conclusion As a pilot research, our results may provide valuable insights for further experimental studies. Meanwhile, our research method combining computational modeling and data from functional experiments can be worthwhile for knowledge discovery; and it can be generalized and extended to other biological/biomedical studies. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0467-4) contains supplementary material, which is available to authorized users.