Immediately following CHI's Second Annual Microarray Data Analysis
September 10-11, 2002, Renaissance Washington DC Hotel, Washington, D.C.
Corporate Sponsor:
| Sponsoring Publications: |
|
Genome Research
Pharmacogenomics |
| Web Partners: |
|
Lab-on-a-Chip.com Pharmacogenomicsonline.com |
A picture is worth a thousand words and provides instant recognition. Data analysis involves visual, as well as statistical, understanding. Today complete biological data analysis involves a team effort including biological researchers, statisticians, bioinformaticists, database developers, and software engineers. This program is designed to incorporate each specialty into a comprehensive unit to produce yet another beneficial avenue for understanding the overwhelming information produced by biologic research.
SCIENTIFIC ADVISORS
Dr. Bruce Aronow, University of Cincinnati and Children's Hospital Medical
Center
Dr. Georges G. Grinstein, University of Massachusetts and AnVil
Dr. C. Bret Jessee, AnVil
SPEAKERS
Dr. Christopher Ahlberg, Spotfire, Inc.
Dr. Bruce Aronow, University of Cincinnati and Children's Hospital
Medical Center
Dr. Joanna L. Batstone, IBM Corporation
Dr. Charles Berger, Oracle Corporation
Dr. Christopher M.L.S. Bouton, LION Bioscience Research, Inc.
Dr. Atul Butte, Children's Hospital, Boston
Dr. Daniel B. Carr, George Mason University
Dr. Dianne Cook, Iowa State University
Dr. Sorin Draghici, Wayne State University
Dr. Reinhard Ebner, Avalon Pharmaceuticals, Inc.
Dr. Madeleine Gross, Silicon Genetics
Dr. Bruce Hoff, BioDiscovery, Inc.
Dr. Christine Lemke, MelTec GmbH
Dr. Michael N. Liebman, University of Pennsylvania Cancer Center
Dr. Michael McManus, AnVil
Dr. Edward Moler, Chiron Corporation
Dr. Philip J. Monroe, OmniViz, Inc.
Dr. Isaac M. Neuhaus, Bristol Myers-Squibb
Dr. Eric Neumann, Beyond Genomics, Inc.
Dr. Jack Pollard, 3rd Millennium, Inc.
Dr. Bruno W.S. Sobral, Virginia Tech
Dr. Jeff Voss, Abbott Bioresearch Center
Dr. Thomas Waschulzik, iSenseIt AG
Dr. Margaret Werner-Washburne, University of New Mexico
Dr. Russell D. Wolfinger, SAS Institute Inc.
Dr. Qiandong Zeng, GeneData, Inc.
KEYNOTE PRESENTATION
Visual Representation in Data Analysis and Management
DATA VISUALIZATION
Visualizing Gene Regulation
Cluster Analysis
Visualization Tools for Microarrays
3-D Data Visualization and Interpretation
DATA MINING
Creating a Collaborative Environment
Experimental Design Issues
Cross-Referencing Biological Information
Enterprisewide Data Management
Life Sciences Discovery Platform
High-Dimensional Analysis and Visualization
Tools for Experimental Design
DATA INTEGRATION
Visualization of Biological Pathways
Semantic Data Integration
Integrated Data Visualization Techniques
Taking a Chance from Integrated Solutions
Comparison of Genome-Scale Data Sets
Small-Molecule Drug Discovery Process
Data and Application Integration
Customizing Data Integration
DATA INTERPRETATION
Accelerating Drug Discovery
Transcript Profiling
Systems Biology Approach
Analyzing Data within a Biological Context
Compensation for the Changing Information
Addressing Critical Issues
PRECONFERENCE SHORT COURSE
Interactive Data Visualization and Exploration
WEDNESDAY, SEPTEMBER 11
|
5:45-8:45 Preconference Short Course
*Separate
Registration Required |
6:00-8:00 Early Conference Registration and Poster and Exhibit Set-up
THURSDAY, SEPTEMBER 12
7:30am Conference Registration and Poster and Exhibit Viewing, with Light Continental Breakfast
DATA VISUALIZATION
8:30 Chair's Opening Comments
Dr. Georges G. Grinstein
| 8:40
Keynote Presentation: Visual Representation in Data Analysis and Management Dr. Michael N. Liebman, Director, Computational Biology, and Investigator, Abramson Family Cancer Research Institute, University of Pennsylvania Cancer Center Because of the large quantities and complexities of the biological data, from sequence to expression, genomics to proteomics, scientists often are concerned more with capturing and linking it than in its integrated analysis. Complex data analysis is frequently focused on visualization that reflects a conventional perspective with additional information content provided by highlights, e.g., colors, intensities. While these forms of representation present some degree of comfort in their ability to readily reproduce what the viewers expect to see, they can be limiting in their ability to be used for identifying new, even more complex relationships. We have been developing and implementing methods for data representation that involve data mapping and the conversion of readily identifiable patterns to more complex formats that exhibit different weighting of the underlying information. These representations have been applied from the molecular level through the pathway level and will be discussed in terms of their ability to contribute new insights into the underlying organization at the physiological level and its importance in biological function. |
9:15 Visualizing Gene Regulation and Peptide
Docking Statistics
Dr. Daniel B. Carr, Professor of Statistics, Department of Applied and
Engineering Statistics, George Mason University
This talk presents a 3-D rendering approach for visualizing letter-indexed
statistics. The approach begins by using self-similar geometric structures to
construct unique plotting coordinates for short sequences. Encodings for counts
or likelihoods indexed by longer sequences include the radius and color of point
connecting tubes. Interactive filtering options provide focus on the dominant
structures residing within the huge combinatorial space. A gene regulation
example renders counts indexed by nucleotide sequences and an immune system
peptide docking example renders multiway tables indexed by amino acid and
position combinations.
9:45 Guided Analytics of Gene Expression
Information.
Dr. Christopher Ahlberg, Chief Executive Officer, Spotfire, Inc.
The last decade has seen an abundance of novel technologies,
methodologies,and research content coming into the domain of biological
research. Novel visualization and analytic technologies have been successful in
battling this explosion - allowing researchers who otherwise would be confined
to spreadsheets to best understand the ever accumulating data. While these
technologies have had big impact I will argue that to see real improvements in
research productivity we need to see a discontinuous change in how research
organizations deal with decision-making and similarly a discontinuous change in
software strategy for decision-making. I will outline a novel approach to visual
and analytical software for the world of biological research- building on
previous success in data visualization.
10:15 Poster and Exhibit Viewing, Refreshment Break
10:45 Visualization Tools for Microarrays
Dr. Dianne Cook, Department of Statistics, Iowa State University
This presentation will discuss graphical methods for exploring microarray
data. The emphasis is on interactive and dynamic graphics for exploring
multivariate relationships. The methods will be demonstrated on data from
experiments on Arabidopsis plants.
11:15 3-D Data Visualization and
Interpretation Technology for High-Throughput Proteomics Characterization
Dr. Christine Lemke, Chief Operating Officer, MelTec GmbH
Cellular function and response to external stimuli are determined not simply
by the mere presence of proteins but by their spatial distribution and
interaction. Cells tightly regulate the relative protein location, enabling
proteins to form interactive networks, which lead to specific cell activity.
Therefore, different spatial distributions of the same proteins can encode
different cellular functions. MelTec's high-throughput robotic imaging
technology, MELK, combines cell biology and biomathematical tools to visualize
protein networks at the cellular and subcellular levels without disrupting the
cell's integrity; correlate the networks with biological function; and develop
topological protein maps of cells that are healthy and of cells influenced by
disease, drug effect, or environmental stimuli. MELK is a unique technology
capable of performing completely automated proteomics characterization of single
cells for an almost unlimited number of proteins simultaneously.
11:45 Panel Discussion with Questions from Audience for Morning Speakers
12:15 Lunch (on your own)
DATA MINING
1:30 Chair's Comments
Dr. Bruce Aronow, University of Cincinnati and Children's Hospital
Medical Center
1:35 A Collaborative Software Environment for
Microarray Data Analysis
Dr. Russell D. Wolfinger, Director of Genomics, SAS Institute Inc.
In the context of a real data example, we discuss critical interactions that
must take place between scientists, statisticians, IT administrators, and
bioinformaticists to ensure rapid and successful scientific discovery. We place
particular emphasis on the statistician who, via both personal interaction and
software coding, provides expert guidance in areas such as experimental design,
data modeling, and analytical inference. Common but well-chosen graphical
displays and interfaces play a central role and facilitate efficient
communication between personas.
2:05 Experimental Design Issues in the
Creation and Mining of Gene Expression Databases
Dr. Bruce Aronow
Microarray experiments measure the expression of an ensemble of genes
sampled over a series of experimental conditions or biological states. Useful
knowledge can be derived from an understanding of the relatedness of individual
gene or gene-group behaviors as well as the relationships of different
biological states. An ideal gene expression database would depict the
transcriptional output of the entire genome over all possible biostates. Two
critical issues in the generation of large-scale expression databases are
methods for quality assurance and the selection of sufficiently variant
biological samples that allow for a high-resolution discrimination of genes that
participate in specific biological processes and the pathways that control the
processes. I will describe two different experimental designs, and their
capabilities for resolving molecular interrelationships will be described. A
tissue-type and cell-type-specific experimental design provides the opportunity
for inter-comparison of widely divergent biological states and transitions
associated with gene expression in differentiation and development. An
alternative experimental design will be shown that gives a very high-resolution
view of pathways and components that comprise immuno-inflammatory system
dynamics.
2:35 The Power of Visualization Tools to
Cross-Reference Biological Information and Clustering Results in Gene Expression
Profiling
Dr. Madeleine Gross, Technical Support Scientist, Silicon Genetics
Initial software development for expression profile analysis focused on the
computational requirements for managing the data structures typical of
microarray studies. Main issues included normalization algorithms and
statistical testing and clustering, as well as graphical representation of the
results. The need for data-mining tools to insert biological annotations to the
expression profiling is the next most compelling requirement for expression data
interpretation. After extracting information from expression profiles and
statistically significant data, it is important to be able to compare those
results to prior biological knowledge. For instance, cross-referencing a set of
genes with interesting expression behavior against known functional classes
might be of great biological significance. We demonstrate how cross-referencing
features can be the basis for formulation of hypotheses about biological
function for poorly characterized genes or about mechanisms of action for poorly
characterized physiological conditions. The analysis of a real data set will be
performed using GeneSpring™ software, an analytical workbench for genomic
expression analysis.
3:05 Enterprisewide Data Management and
Analysis for Microarrays
Dr. Bruce Hoff, Director of Analytical Sciences, BioDiscovery, Inc.
The advances in microarray technology have generated a vast amount of data,
in areas ranging from laboratory information management systems (LIMS), to image
analysis, to genomic information, to statistical analysis, along with creating a
need for supporting tools for data gathering, analysis, and visualization. In
this presentation, we will describe GeneDirector, an enterprisewide data
management system. We use real microarray research examples to demonstrate the
usage of the system. In particular, we will (1) use LIMS information about the
experimental set-up to formulate subsequent normalization and analysis of
expression data; (2) retain quality information generated at the
image-processing stage, for later use during expression data analysis; (3)
control the data normalization and filtering used in families of experiments,
such that later data-mining efforts span only comparable data; and (4) find
differentially upregulated genes based on comparable data prepared above.
3:35 Poster and Exhibit Viewing, Refreshment Break
4:00 Life Sciences Discovery Platform
Dr. Charles Berger, Senior Director of Product Management, Life Sciences
and Data Mining Technology, Oracle Corporation
As we completed the mapping of the human genome, biology has evolved to the
sciences of information. Pharmaceutical and biotech companies that are the most
successful at managing and processing life-science data and quickly extracting
valuable new insights will be the ones that succeed. This presentation will
highlight features in the Oracle 9i database and application server that solve
challenges the life-sciences industry is facing today. We will also show some
examples on how people can use Oracle9i Data Mining for cancer research.
4:30 Predictive Toxicology Using
High-Dimensional Analysis and Visualization
Dr. Michael McManus, Executive Vice President and Chief Operating
Officer, AnVil
High-throughout screening (HTS) technologies have greatly accelerated the
rate at which multivariate experiments can be performed and have yielded truly
massive quantities of data for analysis and interpretation in predicting
candidate drug compound activity and toxicity. We present an example of coupled
analytical and visual high-dimensional data-mining processes used to discover
structural and chemical property determinants of toxicity. Using the RadViz™
visual analytic algorithm, in conjunction with association rules, genetic
algorithms, and other analytical methods, we demonstrate how high-dimensional
data-mining techniques can rapidly yield discrimination of structural parameters
and properties associated with potential drug activity and toxicity.
5:00 Microarray Data Analysis - Onto-Express
and Functional Analysis as a Tool for Experiment Design
Dr. Sorin Draghici, Assistant Professor, Dept. of Computer Science, Wayne
State University
A plethora of tools for sifting through the large amount of raw data exists
for image processing, pre-processing, clustering and calculating statistical
confidence. However, no matter how carefully an experiment is carried out in the
laboratory, no valid conclusions can be drawn unless the experiment is designed
properly. The validity of any conclusions drawn from a microarray experiment may
be limited by the genes present on the chip. If these genes do not represent
properly the particular biological process under study, the usefulness of the
whole microarray experiment might be severely limited. This talk will focus on a
functional approach to experiment design. This can be done with Onto-Express
which is a new tool for functional analysis. Onto-Express can be used both for
selecting the chip or combination of chips that are best for a given experiment
as well as for the functional analysis of the results provided by the chips. The
talk will present the tool as well as examples showing its utility. A free
version of Onto-Express is available at http://vortex.cs.wayne.edu:Projects.html
5:30 Panel Discussion with Questions from Audience for Afternoon Speakers
6:00 Networking Reception (sponsored by Cambridge Healthtech Institute)
7:00 Close of Day One
FRIDAY, SEPTEMBER 13
7:30am Poster and Exhibit Viewing with Light Continental Breakfast
DATA INTEGRATION
8:00 Chair's Comments
Dr. Bruno W.S. Sobral, Professor and Director, The Virginia
Bioinformatics Institute, Virginia Tech
8:05 Visualization and Data Integration using
Metabolic Networks
Dr. Bruno W.S. Sobral
There is a growing amount of -omics data that can be used to reverse
engineer living systems for the purposes of building models that predict
organismal performance in a specified environment. A functional understanding of
living organisms requires not only a parts list, such as developed through
genomics, but also a quantitative description of the organism's state. The state
data, from transcriptomics (mRNA), proteomics, and metabolomics, will be
required to understand the dynamics of living systems. We will show work that
deals with the integrated visualization of some of these data. For example, gene
expression and/or proteomics data are overlaid with metabolomics data. In such
cases, chemistry is the link between data types.
8:35 Semantic Data Integration: Delivering on
the Promise of High-Throughput Biology
Dr. Jack Pollard, Principal Investigator, Bioinformatics, 3rd Millennium,
Inc.
Functional genomic technologies and the systems biology approach promise to
revolutionize drug discovery by facilitating the construction of biological
pathway and network models. However, a massive volume of diverse data types must
be semantically integrated in order to build these models, and semantic data
integration has proven a major obstacle. We have developed a semantic
integration technology driven by ontologies for researchers studying biological
pathways and the underlying networks. The technology is capable of extracting
and integrating data from a variety of genomic, interaction, and pathway
sources. By describing biological objects and the interactions in which they
participate in a directed graph-based paradigm, researchers can model data
according to their own semantic views. A unique feature of our technology is
that it allows scientists to define their own integration processes for the data
thereby making multiple views and integrations possible from the same underlying
data. This discussion will detail how our approach allows users to query,
visualize, and reconstruct biological pathways.
9:05 Using Integrated Data Visualization
Techniques and Analysis Tools in Genomics Research
Dr. Philip J. Monroe, Senior Application Scientist, OmniViz, Inc.
The genomics researcher is confronted with the task of analyzing large
volumes of data (which are often obtained in different formats from multiple
sources), as well as integrating this analysis with information contained in
current knowledge bases. Furthermore, the analysis must be presented in a way a
biologist can readily understand the results. One approach to solving this
dilemma is through the use of data visualization techniques. However, there is
no "right" way to visualize data; that is, no single visualization can
convey all the relevant features of the data. Consequently, the researcher needs
to be able to access multiple visualization techniques that are geared towards
answering different data analysis questions through the presentation of the data
from a number of different perspectives. Furthermore, the integration of these
multiple visualizations is critical. Moreover, access to internal and external
databases/knowledge bases during the data analysis is required for a more
integrated, "global" perspective that takes advantage of all available
information. Examples of different visualization techniques that can be used to
analyze data commonly encountered by the genomics researcher will be presented.
An analysis of gene expression data within individual experimental conditions,
then across all experimental conditions, as well as integrating the experimental
results with an analysis of the scientific literature, will be described.
Moreover, the utility of the integration of the results with information
obtained from other knowledge bases will also be discussed with respect to
facilitating a more informed scientific and business decision-making process.
9:35 Taking a Chance from Integrated Solutions
for the Interpretation of DNA Microarray Experiments
Dr. Thomas Waschulzik, Chief Executive Officer, iSenseIt AG
iSenseIt has developed an integrated software platform for DNA microarrays
incorporating task specification and sequence handling, chip configuration
including oligo design, quality assurance, normalization, interpretation, and
data mining in order to offer our customers comprehensive problem-free
solutions. With the design of the chip, a component for an inference algorithm
is generated that allows getting answers from the system according to its
specified task. These answers may be the identification of a gene from a gene
family taking into account cross-hybridizations or the solution of complex tasks
like the identification of the genotype of hepatitis C viruses. To get these
answers thermodynamic models are used. In the inference process the information
of different oligos is combined with techniques from the field of artificial
intelligence research. All this is available in a commercial product.
10:05 Poster and Exhibit Viewing, Refreshment Break
10:30 Use of the Visualization and Clustering
Tool, VxInsight, for Comparison of Genome-Scale Data Sets
Dr. Margaret Werner-Washburne, Biology Department, University of New
Mexico
As genome-scale data sets from different levels of analysis become
available, tools for extracting valuable information that exists within and
between the data sets become increasingly important. We have developed methods
using VxInsight, a clustering and visualization tool, for integrating data from
different levels of genomic analyses; increasing the speed with which
information can be analyzed by biologists; and, perhaps most importantly,
providing rapid feedback for hypothesis development and experimental design.
11:00 Data Generation, Visualization, and
Analysis in the Small-Molecule Drug Discovery Process: The Changing Needs of a
Rapidly Expanding Operation
Dr. Reinhard Ebner, Principal Scientist, Genomics, Avalon
Pharmaceuticals, Inc.
Avalon Pharmaceuticals is a young drug discovery company with a mission to
develop innovative small molecule therapeutics using cutting-edge genomic
technologies. By attracting a leading team of innovative scientists and drug
discovery experts, utilizing genomic tools, and forming strategic alliances
within the industry, we are significantly decreasing the time it takes to
develop novel drug therapies to treat diseases. Avalon's strategy of combining
the latest genomic approaches for gene and target identification with an
aggressive pursuit of small-molecule drug discovery has attracted significant
interest from the pharmaceutical industry. Avalon is discovering novel
therapeutics against new and unknown drug targets by screening large libraries
of small-molecule chemicals for their effect on proprietary drug activity
markers in multiparameter disease model systems. This approach permits the
screening of drug effects against many different targets in parallel without the
need for detailed information about each target. Size and diversity of the
readouts from our screening have generated increasing demands for the
integration, analysis, and visualization of large, complex data sets. We will
outline some of our early requirements and initial simple solutions as well as
describe our growing needs and long-term vision.
11:30 Data and Application Integration for
Drug Discovery and Development
Dr. Joanna L. Batstone, Senior Manager, Life Sciences Solutions
Development, IBM Corporation
Advances in bioinformatics and cheminformatics are enabling companies to
leverage information technology across the drug development process. The promise
of shorter research and development cycle times and enhanced cost efficiencies
can result in more products reaching the marketplace. However, life sciences
data-management requirements are growing faster than Moore's Law and, as a
result, we're seeing an exponential increase in the amount of data and an
exponential increase in the computing power and storage required to generate and
manage these data; the IT market in life sciences is exploding. In this
presentation, we will highlight IBM's solution offerings for data management and
integration for the integration of data and applications to support the
collaboration of scientists in the pharmaceutical, medical research,
bioinformatics, and diagnostics segments. We will present examples of customer
experiences with DiscoveryLink, IBM's data integration solution for life
sciences. DiscoveryLink is a component of the IBM Life Sciences Framework that
enables the integration of both applications and data via open standards such as
XML and web services.
12:00 Customizing Data Integration
Dr. Isaac M. Neuhaus, Senior Scientist, Bioinformatics, Bristol
Myers-Squibb
Every big pharmaceutical company has to deal with the fact of integrating
terabytes of information generated after the big 'omics' revolution. The task is
even more complicated given that the technologies are still evolving at a fast
pace, the data is not stable and the relationships are not well established. We
have focused our efforts in helping our scientist identifying and finding the
relevant information by tailoring the integration between specialized
technologies. We believe that until we come across a global solution system with
the flexibility that we require this is the most effective strategy in the drug
discovery business.
12:30 Panel Discussion with Questions from Audience for Morning Speakers
1:00 Luncheon (sponsored by Cambridge Healthtech Institute)
DATA INTERPRETATION
2:00 Chair's Comments
Dr. Michael McManus
2:05 Accelerating Anti-Infective Drug
Discovery through Integrated Expression Profile Analysis and in silico
Functional Genomics
Dr. Qiandong Zeng, Senior Bioinformatics Specialist, GeneData (USA), Inc.
GeneData has developed a comprehensive suite of integrated software systems
to tackle the typical problems pharma companies are facing in data quality
assessment and data analysis of genome, transcriptome, proteome, metabolome, and
high-throughput screening data. We demonstrate how gene expression analysis in
combination with other genomics technologies leads to new functional insights
that accelerate the drug discovery process. We discuss examples of complex
biological data sets with the purpose of predicting protein functions, metabolic
pathways, and understanding mode-of-actions of novel structural classes of
antibiotics compounds. We use a standardized collection process of expression
data to establish a reference compendium that can be further used to predict
MOAs based on large-scale DNA chip experiments. We will also show the power of
combining sequence analysis with gene expression analysis to understand the
effect of drug candidates on biochemical pathways, in order to prioritize drug
development.
2:35 Measuring the Selectivity of Lead
Compounds Using Transcript Profiling: Supporting Discovery and Reducing
Attrition
Dr. Jeff Voss, Group Leader, Genomics & Bioinformatics, Abbott
Bioresearch Center
Biochemistry and histology have long been used to measure the
pharmacodynamics and toxicity of drug candidates. Although these can rank and
compare compounds, often little is learned about the mechanisms of on- and
off-target effects. We are applying transcript profiling to animal models to
understand a compound's mechanism of action, selectivity, and the basis of side
effects. Among our challenges is efficiently assigning substantiated relevant
functions to implicated genes and connecting gene clusters to metabolic and
regulatory pathways. We also strive to discover better ways to convey
distillations of complex data sets back to our end-user pharmacologists and
medicinal chemists.
3:05 Closing the Loop: A Systems Biology
Approach to Experiment Design, Data Integration, Pathway Model Building, and New
Hypothesis Generation
Dr. Eric Neumann, Vice President, Bioinformatics, Beyond Genomics, Inc.
A Systems biology approach to drug discovery requires integration of several
different data types, including gene expression, protein, and metabolite levels.
Dealing with such different types of data presents several challenges for data
integration and model building. Our approach to discovery is cyclic; from
experiment design, data collection, normalization, integration, and model
building to creating new hypotheses and returning to design of new experiments.
By using such an approach we iteratively assemble our knowledge about the system
under consideration using both experimental data and our existing databases and
ontologies. As an example, we will discuss our recent study of the APO*E3
transgenic mouse model, which offers researchers a model for the onset and
progression of atherosclerosis.
3:35 Poster and Exhibit Viewing, Refreshment Break
4:00 Analyzing Large-Scale Gene Expression
Data within a Biological Context Using DRAGON and DRAGON View
Dr. Christopher M.L.S. Bouton, Bioinformatician, LION Bioscience
Research, Inc.
Large-scale gene expression data can be annotated with various types of
biological information in a simultaneous, comprehensive fashion using the DRAGON
database (Bouton and Pevsner, 2000). Preannotation of expression data sets
allows for novel forms of analysis that incorporate biologically relevant
information such as encoded protein domains, participation in cellular pathways
and chromosomal location. DRAGON View (Bouton and Pevsner, 2002) provides a set
of web-accessible tools that aid in the analysis of biologically annotated
expression data sets. Using example data I will demonstrate the use of both
DRAGON and DRAGON View and show how they can be used to conduct novel forms of
data analysis within a biological context.
4:30 Novel Visualization Techniques in
Functional Genomics
Dr. Atul Butte, Assistant in Endocrinology and Informatics, and Attending
Physician, Children's Hospital, Boston
Microarrays can provide systematic quantitative information on the
expression of thousands of unique RNAs. I will survey 25 published visualization
methods in functional genomics, focusing on graph-representations of potential
biological pathways. Having analyzed over 600 microarrays, I will provide the
following examples. (1) Not all pathways will be reverse engineered and
visualized using microarrays. (2) Looking for simultaneous gene associations
ignores the fact that biology takes time and visualization need to take
advantage of temporality. (3) Due to rapidly changing information about the
genes already measured (names, functions, genome positions), all visualization
techniques need to compensate for the changing information behind our graphs.
5:00 Cleaning the Pipe: Critical Issues with
Data Integrity, Analysis, and Interpretation in High-Throughput Screening
Experiments
Dr. Edward Moler, Senior Scientist, Molecular Discovery and Cancer
Genomics, Chiron Corporation
Functional genomics screens, including cDNA and oligonucleotide arrays, are
core platforms that are being increasingly employed at all stages of discovery,
development, and testing. The high-throughput screening experiments are
typically carried out by specialized teams who develop expertise in specific
platforms, while the results are used by a large audience of biologists,
chemists, pharmacologists, clinicians, and even members of the business and
legal departments, e.g., for evaluating licensing opportunities. Because of the
complexities inherent in these technologies, there are many opportunities for
errors in executing the experiments, reducing and summarizing the data,
annotating the results, and interpreting the meaning of the results and their
visualizations. I will address the challenges and pitfalls of ensuring data
integrity and proper interpretation and the use of statistical analyses and
visualization in meeting these challenges.
5:30 Panel Discussion with Questions from Audience from Afternoon Speakers
6:00 Close of Conference
CORPORATE SPONSOR BIOGRAPHIES
Amersham Biosciences, the life sciences business of Amersham plc (LSE, NYSE, OSE:
AHM), is a world leader in developing and providing integrated systems and
solutions for disease research, drug development and manufacture. Our vision is
to enable molecular medicine.
Gene Logic Inc. is a leading genomics-based biocontent and bioinformatics
company focused on developing information products related to gene activity in
human disease and toxicity to optimize rapid, reliable and cost-effective
pharmaceutical and biotechnology research and development. Through its expertise
in biosamples, high-throughput genomics technologies and software development,
the Company has developed a series of gene expression information solutions
based on its core product, the GeneExpress® Suite.
Iobion Informatics presents GeneTraffic software for two-color microarray data
management and analysis. GeneTraffic software allows you to access data and
projects on a desktop PC, from any location within your Network, manage data,
perform computational analyses and query your data. With GeneTraffic software
you can qualify and validate your microarray data prior to biological analysis. Iobion Informatics is a Delaware LLC, headquartered in La Jolla, CA with offices
in Toronto, Canada, and Austin, Texas.
HOTEL INFORMATION
Renaissance Washington DC Hotel
999 Ninth Street, N.W.
Washington, DC 20001
T: 202-898-9000
F: 202-289-0947
Room Rate: $199 S/D
Cut-off Date: August 15, 2002
Please call the hotel directly to make your room reservation. Identify yourself as a Cambridge Healthtech Institute conference attendee to receive the reduced room rate. Reservations made after the cut-off date or after the group room block has been filled (whichever comes first) will be accepted on a space-and-rate-availability basis. Rooms are limited, so please book early.
TRAVEL INFORMATION
Special Zone and Discount Fares have been established for this conference
with United Airlines. Please call United Airlines Meeting Reservation Desk at
800-521-4041 and reference ID #579YS.
CALL FOR SPONSORS AND EXHIBITORS
This conference will present the latest techniques for data analysis via
visualization and serve as excellent follow-up to the preceding Microarray
Data Analysis meeting. We strongly encourage any company with services or
products related to microarrays, microarray readers, data visualization, image
analysis, informatics, data mining, data storage & retrieval, to consider
sponsoring or exhibiting at this event.
For more information on sponsorship opportunities or to
reserve a booth, please contact Angela Parsons at 781-972-5467 or aparsons@healthtech.com.
The following companies are exhibiting as of July 17, 2003
| Data Visualization | Booth # |
| Affymetrix | 123 |
| BioDiscovery | 107 |
| Gene Logic | 143 |
| GeneData, Inc. | 119 |
| Insightful Corp. | 115 |
| Iobion Informatics LLC | 117 |
| MolMine AS | 137 |
| NDRI | 129 |
| Silicon Genetics | 113 |
| Spotfire, Inc. | 141 |
CALL FOR POSTERS
Cambridge Healthtech Institute encourages attendees to gain further
exposure by presenting their work in the poster sessions. Please fill out the
registration form, with the poster title and primary author. To ensure
inclusion in the conference binder, a one-page summary must be submitted and
registration must be paid in full by August 9, 2002. Click
here for poster instructions
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