Kim-Anh Do

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MOTIVATION Analyzing data from multi-platform genomics experiments combined with patients' clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental(More)
Translation initiation is activated in cancer through increase in eukaryotic initiation factor 4E (eIF4E), eIF4G, phosphorylated eIF4E-binding protein (p4E-BP1) and phosphorylated ribosomal protein S6 (pS6), and decreased programmed cell death protein 4 (pdcd4), a translational inhibitor. Further, translation elongation is deregulated though alterations in(More)
BACKGROUND The early detection of prostate cancer has resulted in an increase in the number of patients with localized prostate cancer and has paralleled the reported reduction in prostate cancer mortality. The increased rate of detection of patients with localized prostate cancer may also increase the risk of potentially morbid therapy in a patient with(More)
Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated(More)
In order to better understand cancer as a complex disease with multiple genetic and epigenetic factors, it is vital to model the fundamental biological relationships among these alterations as well as their relationships with important clinical outcomes. We develop an i ntegrative net work-based Bayesian analysis (iNET) approach that allows us to jointly(More)
We tested the antitumor efficacy of mTOR catalytic site inhibitor MLN0128 in models with intrinsic or acquired rapamycin-resistance. Cell lines that were intrinsically rapamycin-resistant as well as those that were intrinsically rapamycin-sensitive were sensitive to MLN0128 in vitro. MLN0128 inhibited both mTORC1 and mTORC2 signaling, with more robust(More)
We review the use of semi-parametric mixture models for Bayesian inference in high throughput genomic data. We discuss three specific approaches for microarray data, for protein mass spectrometry experiments , and for SAGE data. For the microarray data and the protein mass spectrometry we assume group comparison experiments, i.e., experiments that seek to(More)
Considerable progress has been made on algorithms for learning the structure of Bayesian networks from data. Model averaging by using bootstrap replicates with feature selection by thresholding is a widely used solution for learning features with high confidence. Yet, in the context of limited data many questions remain unanswered. What scoring functions(More)
BACKGROUND In the Prostate Cancer Prevention Trial, finasteride selectively suppressed low-grade prostate cancer and significantly reduced the incidence of prostate cancer in men treated with finasteride compared with placebo. However, an apparent increase in high-grade disease was also observed among men randomized to finasteride. We aimed to determine why(More)