Current Students and Research

Valentin works on disease-oriented analysis of high-density microarray genomic data and on clinical database modeling. He uses Affymetrix 100K and 500K SNP GeneChips to investigate the association of genomic loci with complex diseases, such as age related macular degeneration and cardiac diseases. He is also investigating copy number changes in cancer using Affymetrix, Nimblegen, and Illumina high-density genomic microarrays. One particular focus of his research is developing tools for pathway-based association analysis of genomic data. He is also investigating issues related to the use of the entity-attribute-value modeling approach to clinical databases, such as improving performance through in-memory data processing.

Sujun researches on steroid hormone signaling pathways and breast cancer. Specifically, his research focuses on

1. identifying components of the estrogen signaling pathways in breast cancer cells
2. understanding the genetic and epigentic aspects of the hormone reponse network in breast cancer cells
3. attempting to understand the patways inolved in breast cancer progression using genome-wide RNAi screening

Yin works on developing statistical methods to analyze large-scale genomics and proteomics data and applies these methods to study biological problems. In particular, she developed statistical approaches for the genome-wide protein interaction prediction and protein complex identification in yeast. The protein interaction data and gene expression data from microarray chips have been integrated for signal trasndcution pathways reconstruction. Yin is also working on the identification of allelic association between genetic variations located on different chromosomes using human HapMap project data.

ThaiBinh's current project involves mining through biomedical literature in order to map instances of gene strings and diseases to a respository of gene/disease identifiers. The aim of this process is to more easily classify research papers and identify relevant papers for researchers.

Sara researches protein structure sampling algorithms for applications such as structure determination and protein-protein or protein-ligand interactions. Her research focuses on methods to explore the energy surface more efficiently than standard Monte Carlo sampling algorithms. She is interested in secondary structure initiation, side chain sampling, and global optimization for protein folding.

Jill's research focuses on epigenetic markers, such as methylation. In particular, she studies the mapping and analysis of these markers on a genome wide scale. The main goals of this work are:

1. to explore the potential of epigenetic data to screen for disease susceptibility and predict drug response
2. to create a tool that helps to explain the epigenetic mechanisms that regulate chromatin conformation and gene expression. This tool uses zinc finger proteins designed to bind to target sequences only where the DNA is open and accessible.

Xiaowei's research focuses on two main areas:

1. studying transcription factors in yeast, particularly complexes of these factors. He wishes to predict combinatorial modules and further characterize their regulatory role.
2. data analysis in protein microarrays. He is designing a scaling algorithm for noise subtraction and signal normalization, and also developing data mining techniques for biological networks and disease profiling datasets.