Tim Huang

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With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated,(More)
Prostate cancer cells escape growth inhibition from transforming growth factor β (TGFβ) by downregulating TGFβ receptors. However, the mechanism by which cancer cells downregulate TGFβ receptors in prostate is not clear. Here, we showed that coordinated action of miR-21 and androgen receptor (AR) signaling had a critical role in inhibiting TGFβ receptor II(More)
The endocycle is a variant cell cycle consisting of successive DNA synthesis and gap phases that yield highly polyploid cells. Although essential for metazoan development, relatively little is known about its control or physiologic role in mammals. Using lineage-specific cre mice we identified two opposing arms of the E2F program, one driven by canonical(More)
BACKGROUND CCAAT/Enhancer Binding Proteindelta (C/EBPdelta) is a member of the highly conserved C/EBP family of leucine zipper (bZIP) proteins. C/EBPdelta is highly expressed in G0 growth arrested mammary epithelial cells (MECs) and "loss of function" alterations in C/EBPdelta have been associated with impaired contact inhibition, increased genomic(More)
RNA polymerase II (PolII) is essential in gene transcription and ChIP-seq experiments have been used to study PolII binding patterns over the entire genome. However, since PolII enriched regions in the genome can be very long, existing peak finding algorithms for ChIP-seq data are not adequate for identifying such long regions. Here we propose an enriched(More)
Next Generation Sequencing is highly resource intensive. NGS Tasks related to data processing, management and analysis require high-end computing servers or even clusters. Additionally, processing NGS experiments requires suitable storage space and significant manual interaction. At The Ohio State University's Biomedical Informatics Shared Resource, we(More)
Typical analysis of time-series gene expression data such as clustering or graphical models cannot distinguish between early and later drug responsive gene targets in cancer cells. However, these genes would represent good candidate biomarkers. We propose a new model - the dynamic time order network - to distinguish and connect early and later drug(More)