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此Biomarker Discovery Informatics會議中，從早期醫藥品研究組織，到後期治療效驗組織、NIH及NIST等，生物標誌創藥製程主要的有關人員聚集，討論資訊學、適合個別化臨床之生物標誌開發的內在組織課題。亦討論染色體組（Genomics）、蛋白質體抗體（proteomics）生物標誌特定的課題及解決方法。此外，還介紹了徹底分析生物標誌數據的先進數據挖掘技術。

RECOMMENDED SHORT COURSE*

(SC6) Integration and Analysis of Heterogeneous Translational Data 

*Separate registration required

 

THURSDAY, SEPTEMBER 8

 

12:30 pm Conference Registration

 

INTEGRATED AND RATIONAL APPROACH
FOR BIOMARKER DISCOVERY

1:30 Chairperson's Opening Remarks

Michael Kalos, Ph.D., Director, Translational and Correlative Studies Laboratory, Stellar-Chance Laboratories, University of Pennsylvania

Keynote Presentation 1:40 Biostatistics in Biomarker Discovery: Is the Tail Wagging the Dog? Francesco Marincola, Associate Director, Trans-NIH Center for Human Immunology, National Institutes of Health Biomarker discovery is traditionally based on class comparison applying high stringency parameters. As the stringency is decreased to enhance the chance for discovery, specificity is gradually lost. In complex diseases where combinations of redundant, synergistic or antagonistic functions drive a given phenotype the problem is accentuated. In this presentation, we argue that biological principles driving bio-statistical inference may simplify the approach to biomarker discovery. An example will be provided derived from the analysis of genes relevant to the rejection of melanomas by immune cells.

2:10 Integrated Approach for Biomarker Discovery

Michael Kalos, Ph.D., Director, Translational and Correlative Studies Laboratory, Stellar-Chance Laboratories, University of Pennsylvania

Biomarkers drive in a fundamental manner translational and clinical research. Accordingly, appropriate development of biomarkers is fundamental for the ultimately successful development of new therapeutics. In this presentation we will discuss approaches to support the appropriate development of biomarkers, focusing on three elements: i. quality, ii. comprehensiveness, and iii. integratability of data sets.

2:40 Integrated Biomarker Discovery: An Architectural Blueprint

Janet Siebert, President, CytoAnalytics

To perform integrated biomarker discovery, we must combine data from a variety of sources and platforms. Possible data sources include demographics, longitudinal clinical records, HLA genotypes, and instrumentation platforms such as bead arrays, flow cytometers, and protein arrays. Often, a single tissue sample is divided into multiple aliquots and interrogated by multiple assays. In this presentation we describe an architectural blueprint for combining this heterogeneous data using proven methods and tools from data warehousing, and for applying platform-agnostic data analysis approaches to the integrated whole.

3:10 Sponsored Presentations (Opportunities Available)

3:40 Networking Refreshment Break in the Exhibit Hall with Poster Viewing

4:20 Designing Ontology Vocabulary for Comparing Cell Imaging and Other Experimental Data

John T. Elliott, Ph.D., Research Scientist, NIST/Biochemical Sciences Division

Identifying biomarkers that report cellular status can be facilitated by mining different biological databases and combining the results between comparable experiments. This task is often limited by the lack of common vocabulary terms that both describe experiments in detail and allow intersection between databases. We are exploring the use of a rules-based vocabulary development scheme to build use-case-dependent ontologies that optimize the possibility of data sharing between disparate biology databases. The vocabulary design concepts and an implementation in a cell image database will be presented.

4:50 Emerging Approaches for Integrated Clinical Trial Conduct

Axel Hoos, Medical Lead & Group Director, Immunology & Oncology, Bristol Myers Squibb Company

5:20 Panel Discussion

5:50 Close of Day

6:30-9:00 Dinner Short Courses*

 

*Separate registration required

 

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