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Day 2

Friday, August 26

8:15 Morning Coffee


8:30 Chairís Remarks
Dr. Kenton D. Juhlin, Principal Scientist, Biometrics and Statistical Sciences, Procter & Gamble

8:35 Interactive Visualization of Microarray Data on Pathways
Dr. Georges Grinstein, Professor and Director, Center for Biomolecular and Medical Informatics, University of Massachusetts-Lowell
We have developed an interactive visualization tool to explore microarray data in the context of biological pathways. Users can design (draft, edit, compare) their own pathways and import pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Users can explore their microarray data using the pathway viewer in conjunction with other visualizations.

9:05 Extracting Biological Meaning from Genome-Scale Datasets with DAVID
Dr. Richard Lempicki, Senior Scientist, Laboratory of Immunopathogenesis and Bioinformatics, SAIC-Frederick, Inc. 
We will discuss integrated solutions deployed in DAVID that overcome many of the challenges faced in biological interpretation of genome-scale datasets. The DAVID knowledgebase currently incorporates dozens of functional annotation sources for more than 1.8 million genes/proteins for more than 67,000 species. Gene lists are rapidly grouped into biological categories and remain dynamically linked to primary data and external data repositories. DAVID provides numerous tools to mine functional annotation including one that highlights proteins, from a user imported gene list, on BioCarta and KEGG pathways maps with the option of enhancing the map with highly related proteins or with known interacting proteins. DAVIDís most powerful feature is the ability to cluster groups of highly related genes with corresponding groups of highly related pathways, allowing investigators to quickly visualize gene-to-gene, pathway-to-pathway and gene-to-pathway relationships.

9:35 Two-Dimensional Gene-Metabolite Network Dynamics Using Integrated Omics Analysis
Dr. Edwin Wang, Bioinformaticist, Biotechnology Research Institute, National Research Council Canada 
We present, for the first time of this kind, a construction of a series of two-dimensional development-stage-specific metabolic networks mapped with gene expression information by integrating ESTs, proteomic and microarray data. A comparative analysis of network structural, topological and gene-regulation dynamics of these development-stage-specific networks uncovered: (1) a network core, containing a couple of reactions, exits cross all the stages, is able to recycle intermediate metabolites and also becomes the root of many new pathways; (2) network structure dynamics and gene-expression-network-structure dynamics are observed under the plant seed development; (3) in the late developmental stages of seeds, some pathways develop back-branches that allow converting their metabolites back to the core, suggesting that various kinds of metabolites can be re-distributed to other pathways in the late seed developmental stages.

10:05 Refreshment Break, Poster and Exhibit Viewing

10:45 Using and Mining the GEO/NCBI Repository
Dr. Ron Edgar, Gene Expression Group Leader, National Center for Biotechnology Information 
The Gene Expression Omnibus (GEO) at NCBI is the largest public high-throughput expression data repository in the world. The database has a flexible and open design that allows the storage of virtually any type of high-throughput molecular abundance-measuring technology, including microarray, SAGE, MPSS or protein MS-MS. Effective mining and visualization of these diverse data is, however, a contentious challenge. GEO provides tools to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces. These tools aim to support usability, even for those with little computational or microarray-related analytical expertise.

11:15 PowerArray: Integrated Analysis Environment for High Dimensional Data Analysis
Dr. Kejun Liu, Senior Statistician, Statistical Sciences Department, GlaxoSmithKline 
PowerArray is a software system that uses robust singular value decomposition (RSVD), high dimensional linear modeling (HDLM) and many other techniques for normalization, gene selection, and high level data analysis for any high dimensional data. It has several RSVD based algorithms for Affymetrix signal extraction, pathway analysis and robust principle component analysis. A unique and high-performance visualization system displays rich graphs of millions of records instantly. A fully documented and fully tested version will be released to public on August.

11:45 Interactive Panel Discussion with Morning Speakers

12:15 Lunch (on your own) 



1:30 Chairís Remarks
Dr. David L. Gerhold, Senior Research Fellow, Molecular Investigative Toxicology Department, Merck Research Labs

1:35 A Microarray Centered Infrastructure for Global Data Production, Processing and Presentation in A Large Pharmaceutical Company
Dr. Xiang Yao, Project Lead, Bioinformatics, Drug Discovery, Johnson & Johnson Pharmaceutical R&D
Microarray technology has been widely used in pharmaceutical research. Producing, processing and presenting massive microarray data has also become a new challenge, especially in global companies, like Johnson & Johnson, where data is produced in different centers with high throughput capacity using multiple chip systems, and data is shared by users in many locations. In addition, accurate microarray intensity data, detailed experiment description, comprehensive gene annotation, integration with other pharmaceutical data and a large collection of analysis results are essential. We have created a microarray centered information management system to meet this challenge.

2:05 A Small Number of Genes is Sufficient to Resolve Many Different End Points using Gene Expression
Dr. Georges Natsoulis, Senior Director, Advanced Technology, Iconix Pharmaceuticals 
We have assembled a very large toxicogenomic database. Rats were treated with more than 600 drugs in multiple dose, multiple times and in biological triplicate. Gene expression profiles were collected from up to seven different tissues. More than 200 hematology, clinical chemistry, histopathology and pharmacology assays were performed in the same animals. We systematically mined the gene expression domain of this dataset using an SVM based two-class supervised classification method. More than 300 thoroughly cross-validated linear classifiers (signatures), each composed of an average of 45 genes, were identified. We verified that these signatures resolve distinct and uncorrelated end-points. Some genes recur in a large number of signatures. The occurrence of genes across signatures follows a power law distribution. These genes are therefore forming a scale free network. We can show that the hubs of that network (as few as 400 genes in a given tissue) are sufficient to recreate all signatures with no appreciable loss in classification performance. This finding opens the possibility of creating a multi-endpoint diagnostic device.

2:35 Analysis of P53 Target Genes in Human Genome via Integration of Microarray and Global P53 DNA-Binding Site Analysis
Dr. Suxing Liu, Biological Research-Oncology, Schering-Plough Research Institute 
The p53 protein is a tumor suppressor involved in regulation of the cell cycle and apoptosis. p53 is a sequence-specific transcription factor that binds to DNA as a tetramer and thereby transcriptionally regulates a large number of genes. The central role of p53 as a tumor suppressor protein has attracted substantial interest in defining its mechanism of action or its network. The complete sequence of the human genome, when combined with expression profiling analysis, makes it possible to define gene regulation networks. This presentation will discuss the approaches to identify p53 target genes in the human genome using microarray technology integrated with a global p53 DNA binding site analysis.

3:05 Refreshment Break, Last Chance to View Posters and Exhibits

3:30 Poster Awards

3:45 Expression Profiling of siRNA-Mediated Gene Knockdown
Dr. Emily Anderson, Research Scientist, Dharmacon, Inc. 
The fields of gene function analysis, pathway elucidation, and drug target validation would be greatly enhanced by the union of two distinct technologies, RNAi-mediated gene knockdown and microarray profiling. However, unpredicted effects associated with siRNA delivery and RNAi-mediated off-targeting complicate the analysis of down-stream signatures associated with specific gene knockdown. In the following work, we have performed a detailed study to identify the key parameters (and experimental designs) that facilitate clear identification of primary gene knockdown effects. When these attributes are taken into consideration, studies that combine RNAi and microarray profiling can provide clear directions for functional genomic and drug development studies. 

4:15 Identifying Kidney Toxicity Biomarkers: Using Physiology to Dissect Transcriptional Profiles
Dr. David L. Gerhold, Senior Research Fellow, Molecular Investigative Toxicology Department, Merck Research Labs
We used a Bayesian strategy to identify a panel of transcriptional biomarkers to diagnose kidney toxicity in rats. Independent training- and test- data sets were established in male rats using kidney and non-kidney toxins. Kidney functional measures and histopathology were used to select correlated genes from gene expression microarray data. A composite kidney pathology metric was compared to expression data from selected genes using Q-RT-PCR to evaluate a gene expression metric for diagnosing kidney pathology. 

4:45 Interactive Panel Discussion with Afternoon Speakers

5:15 Close of Conference

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