Chen, Min
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/3634
Min Chen serves as an Associate Professor of Mathematical Sciences. His research interests divide into three areas:
- Statistical genomics and bioinformatics
- Bayesian methods
- Sampling
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Recent Submissions
Item Synchronous Changes of Cortical Thickness and Corresponding White Matter Microstructure During Brain Development Accessed by Diffusion MRI Tractography from Parcellated Cortex(Frontiers Media SA, 2015-12-02) Jeon, Tina; Mishra, Virendra; Ouyang, Minhui; Chen, Min; Huang, Hao; Chen, MinCortical thickness (CT) changes during normal brain development is associated with complicated cellular and molecular processes including synaptic pruning and apoptosis. In parallel, the microstructural enhancement of developmental white matter (WM) axons with their neuronal bodies in the cerebral cortex has been widely reported with measurements of metrics derived from diffusion tensor imaging (DTI), especially fractional anisotropy (FA). We hypothesized that the changes of CT and microstructural enhancement of corresponding axons are highly interacted during development. DTI and T1-weighted images of 50 healthy children and adolescents between the ages of 7 and 25 years were acquired. With the parcellated cortical gyri transformed from T1-weighted images to DTI space as the tractography seeds, probabilistic tracking was performed to delineate the WM fibers traced from specific parcellated cortical regions. CT was measured at certain cortical regions and FA was measured from the WM fibers traced from same cortical regions. The CT of all frontal cortical gyri, including Brodmann areas 4, 6, 8, 9, 10, 11, 44, 45, 46, and 47, decreased significantly and heterogeneously; concurrently, significant, and heterogeneous increases of FA of WM traced from corresponding regions were found. We further revealed significant correlation between the slopes of the CT decrease and the slopes of corresponding WM FA increase in all frontal cortical gyri, suggesting coherent cortical pruning and corresponding WM microstructural enhancement. Such correlation was not found in cortical regions other than frontal cortex. The molecular and cellular mechanisms of these synchronous changes may be associated with overlapping signaling pathways of axonal guidance, synaptic pruning, neuronal apoptosis, and more prevalent interstitial neurons in the prefrontal cortex. Revealing the coherence of cortical and WM structural changes during development may open a new window for understanding the underlying mechanisms of developing brain circuits and structural abnormality associated with mental disorders.Item Identifying CDKN3 Gene Expression as a Prognostic Biomarker in Lung Adenocarcinoma via Meta-Analysis(Libertas Academica Ltd., 2015-03-24) Zang, X.; Chen, Min; Zhou, Y.; Xiao, G.; Xie, Y.; Wang, X.Lung cancer is among the major causes of cancer deaths, and the survival rate of lung cancer patients is extremely low. Recent studies have demonstrated that the gene CDKN3 is related to neoplasia, but in the literature severe controversy exists over whether it is involved in cancer progression or, conversely, tumor inhibition. In this study, we investigated the expression of CDKN3 and its association with prognosis in lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using datasets in Lung Cancer Explorer (LCE; http://qbrc.swmed.edu/lce/). We found that CDKN3 was up-regulated in ADC and SCC compared to normal tissues. We also found that CDKN3 was expressed at a higher level in SCC than in ADC, which was further validated through meta-analysis (coefficient = 2.09, 95% CI = 1.50–2.67, P < 0.0001). In addition, based on meta-analysis for the prognostic value of CDKN3, we found that higher CDKN3 expression was associated with poorer survival outcomes in ADC (HR = 1.65, 95% CI = 1.39–1.96, P < 0.0001), but not in SCC (HR = 1.10, 95% CI = 0.84–1.44, P = 0.494). Our findings indicate that CDKN3 may be a prognostic marker in ADC, though the detailed mechanism is yet to be revealed.Item Joint Conditional Gaussian Graphical Models with Multiple Sources of Genomic Data(2013-12-17) Chun, H.; Chen, Min; Li, B.; Zhao, H.It is challenging to identify meaningful gene networks because biological interactions are often condition-specific and confounded with external factors. It is necessary to integrate multiple sources of genomic data to facilitate network inference. For example, one can jointly model expression datasets measured from multiple tissues with molecular marker data in so-called genetical genomic studies. In this paper, we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological processes based on multiple sources of data. This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization. We apply our approach to a real dataset measuring gene expression in four tissues (kidney, liver, heart, and fat) from recombinant inbred rats. Our approach reveals that the liver tissue has the highest level of tissue-specific gene regulations among genes involved in insulin responsive facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.