Data Integration in the Life Sciences

1. Front Matter

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2. Keynote Presentations

   1. Enabling the Molecular Medicine Revolution Through Network-Centric Biomedicine

      • Kenneth H. Buetow

      Pages 1-2

   2. Phyl-O’Data (POD) from Tree of Life: Integration Challenges from Yellow Slimy Things to Black Crunchy Stuff

      • Junhyong Kim

      Pages 3-5

3. New Architectures and Experience on Using Systems

   1. Automatically Constructing a Directory of Molecular Biology Databases

      • Luciano Barbosa, Sumit Tandon, Juliana Freire

      Pages 6-16

   2. The Allen Brain Atlas: Delivering Neuroscience to the Web on a Genome Wide Scale

      • Chinh Dang, Andrew Sodt, Chris Lau, Brian Youngstrom, Lydia Ng, Leonard Kuan et al.

      Pages 17-26

   3. Toward an Integrated RNA Motif Database

      • Jason T. L. Wang, Dongrong Wen, Bruce A. Shapiro, Katherine G. Herbert, Jing Li, Kaushik Ghosh

      Pages 27-36

   4. B-Fabric: A Data and Application Integration Framework for Life Sciences Research

      • Can Türker, Etzard Stolte, Dieter Joho, Ralph Schlapbach

      Pages 37-47

   5. SWAMI: Integrating Biological Databases and Analysis Tools Within User Friendly Environment

      • Rami Rifaieh, Roger Unwin, Jeremy Carver, Mark A. Miller

      Pages 48-58

   6. \(^{\textrm{\small{my}}}\)Grid and UTOPIA: An Integrated Approach to Enacting and Visualising in Silico Experiments in the Life Sciences

      • Steve Pettifer, Katy Wolstencroft, Pinar Alper, Teresa Attwood, Alain Coletta, Carole Goble et al.

      Pages 59-70

4. Managing and Designing Scientific Workflows

   1. A High-Throughput Bioinformatics Platform for Mass Spectrometry-Based Proteomics

      • Thodoros Topaloglou, Moyez Dharsee, Rob M. Ewing, Yury Bukhman

      Pages 71-88

   2. Bioinformatics Service Reconciliation by Heterogeneous Schema Transformation

      • Lucas Zamboulis, Nigel Martin, Alexandra Poulovassilis

      Pages 89-104

   3. A Formal Model of Dataflow Repositories

      • Jan Hidders, Natalia Kwasnikowska, Jacek Sroka, Jerzy Tyszkiewicz, Jan Van den Bussche

      Pages 105-121

   4. Project Histories: Managing Data Provenance Across Collection-Oriented Scientific Workflow Runs

      • Shawn Bowers, Timothy McPhillips, Martin Wu, Bertram Ludäscher

      Pages 122-138

5. Mapping and Matching Techniques

   1. Fast Approximate Duplicate Detection for 2D-NMR Spectra

      • Björn Egert, Steffen Neumann, Alexander Hinneburg

      Pages 139-155

   2. Ontology – Supported Machine Learning and Decision Support in Biomedicine

      • Alexey Tsymbal, Sonja Zillner, Martin Huber

      Pages 156-171

   3. Instance-Based Matching of Large Life Science Ontologies

      • Toralf Kirsten, Andreas Thor, Erhard Rahm

      Pages 172-187

6. Modeling of Life Science Data

   1. Data Integration and Pattern-Finding in Biological Sequence with TESS’s Annotation Grammar and Extraction Language (AnGEL)

      • Jonathan Schug, Max Mintz, Christian J. Stoeckert Jr.

      Pages 188-203

   2. Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM

      • Yu Zhang, Zhidong Deng, Hongshan Jiang, Peifa Jia

      Pages 204-214

   3. Accelerating Disease Gene Identification Through Integrated SNP Data Analysis

      • Paolo Missier, Suzanne Embury, Conny Hedeler, Mark Greenwood, Joanne Pennock, Andy Brass

      Pages 215-230

7. Annotation in Data Integration

   1. What’s New? What’s Certain? – Scoring Search Results in the Presence of Overlapping Data Sources

      • Philipp Hussels, Silke Trißl, Ulf Leser

      Pages 231-246

Understanding the mechanisms involved in life (e. g. , discovering the biological functionofasetofproteins,inferringtheevolutionofasetofspecies)isbecoming increasinglydependent onprogressmade inmathematics,computer science,and molecular engineering. For the past 30 years, new high-throughput technologies have been developed generating large amounts of data, distributed across many data sources on the Web, with a high degree of semantic heterogeneity and di?erentlevelsofquality. However,onesuchdatasetisnot,byitself,su?cientfor scienti?c discovery. Instead, it must be combined with other data and processed by bioinformatics tools for patterns, similarities, and unusual occurrences to be observed. Both data integration and data mining are thus of paramount importance in life science. DILS 2007 was the fourth in a workshop series that aims at fostering d- cussion, exchange, and innovation in research and development in the areas of data integration and data management for the life sciences. Each previous DILS workshop attracted around 100 researchers from all over the world. This year, the number of submitted papers again increased. The Program Committee - lected 19 papers out of 52 full submissions. The DILS 2007 papers cover a wide spectrum of theoretical and practical issues including scienti?c work?ows, - notation in data integration, mapping and matching techniques, and modeling of life science data. Among the papers, we distinguished 13 papers presenting research on new models, methods, or algorithms and 6 papers presenting imp- mentation of systems or experience with systems in practice. In addition to the presented papers, DILS 2007 featured two keynote talks by Kenneth H. Buetow, National Cancer Institute, and Junhyong Kim, University of Pennsylvania.

• Annotation
• In silico
• architecture
• bioinformatics
• biology
• biomedical data analysis
• clustering
• data analysis
• databases
• genome
• indexing
• instance-based matching
• interoperability
• life sciences
• medical

Policies and ethics