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High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component(More)
UNLABELLED Network component analysis (NCA) is a method to deduce transcription factor (TF) activities and TF-gene regulation control strengths from gene expression data and a TF-gene binding connectivity network. Previously, this method could analyze a maximum number of regulators equal to the total sample size because of the identifiability limit in data(More)
The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain(More)
Complete modeling of metabolic networks is desirable, but it is difficult to accomplish because of the lack of kinetics. As a step toward this goal, we have developed an approach to build an ensemble of dynamic models that reach the same steady state. The models in the ensemble are based on the same mechanistic framework at the elementary reaction level,(More)
PTEN loss or PI3K/AKT signaling pathway activation correlates with human prostate cancer progression and metastasis. However, in preclinical murine models, deletion of Pten alone fails to mimic the significant metastatic burden that frequently accompanies the end stage of human disease. To identify additional pathway alterations that cooperate with PTEN(More)
New therapies for late stage and castration resistant prostate cancer (CRPC) depend on defining unique properties and pathways of cell sub-populations capable of sustaining the net growth of the cancer. One of the best enrichment schemes for isolating the putative stem/progenitor cell from the murine prostate gland is Lin(-);Sca1(+);CD49f(hi) (LSC(hi)),(More)
BACKGROUND One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of(More)
COX-2 is upregulated in pancreatic ductal adenocarcinomas (PDAC). However, how COX-2 promotes PDAC development is unclear. While previous studies have evaluated the efficacy of COX-2 inhibition via the use of nonsteroidal anti-inflammatory drugs (NSAID) or the COX-2 inhibitor celecoxib in PDAC models, none have addressed the cell intrinsic versus(More)
MOTIVATION Data from DNA microarrays and ChIP-chip binding assays often form the basis of transcriptional regulatory analyses. However, experimental noise in both data types combined with environmental dependence and uncorrelation between binding and regulation in ChIP-chip binding data complicate analyses that utilize these complimentary data sources.(More)
OBJECTIVES Pancreatic cysts are a group of lesions with heterogeneous malignant potential. Currently, there are no reliable biomarkers to aid in cyst diagnosis and classification. The objective of this study was to identify potential microRNA (miR) biomarkers in endoscopically acquired pancreatic cyst fluid that could be used to distinguish between benign,(More)