The Role of Ontology in Big Cancer Data

Date: May 12-13, 2015

Venue

• Day 1 (10am-5pm) and Day 2 Morning (9am-noon) will be in NCI's Shady Grove building (9609 Medical Center Dr, Rockville, MD, 20850), in Room 2W-32/34.

• Day 2 Afternoon (1-3pm) will be a public session in the Natcher Conference Center, NIH Building 45, Bethesda, MD 20892

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Goal of the meeting (to be expanded)

To better understand the challenges involved in using big data for cancer research, and to explore the utility of ontologies in addressing these challenges.

Very Rough Draft Partial Schedule

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Tuesday, May 12 in NCI Shady Grove building (9609 Medical Center Dr, Rockville, MD, 20850), Room 2W-32/34

Addressing Cancer Big Data Challenges through Imaging Ontologies

9:00 Ilya Goldberg (NIA) TBD

10:00 Metin Gurcan (Ohio) and John Tomaszewski (Buffalo): How can ontologies help with the big data challenges of pathology imaging?

12:00 Lunch

13:00 Lunch

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Addressing Cancer Big Data Challenges with the Ontology for Biomedical Investigations (OBI)

14:00Chris Stoeckert (Penn): Integration and alignment of ontologies for cancer metadata collection based on OBI

addresses the challenge that cancer research is multidisciplinary and requires standard terminology from multiple domains.

briefly describe OBI. Show how it has been used for collecting clinical and -omic metadata highlighting relevance to cancer data and integration of other ontologies for that purpose.

Gully Burns (UCSD): Applying OBI to cancer pathways via Knowledge Engineering from Experimental Design (KEfED)

addresses the challenge that much of what is known about cancer is only available in publications and requires text mining including the experimental basis for that knowledge.

application of OBI as semantic base for text mining and knowledge engineering.

Philippe Rocca-Serra: How can OBI contribute to unraveling cancer etiology? Scope, Gaps and Future Development of an interoperable semantic resource

addresses the challenge that cancer big data is multi-scale and requires agents to analyze.

demonstrate use of OBI in annotation and data discovery. Address alternatives to OBI and pros and cons.

Wednesday, Mar 13 Morning in NCI Shady Grove building (9609 Medical Center Dr, Rockville, MD, 20850), Room 2W-32/34

Cancer Big Data and the Ontology of Disease

10:00 Lindsay Cowell (UT Southwestern): TBD

Lynn Schriml (Baltimore): TBD

Evan Bolton (NIH / NLM / NCBI): TBD

Themes:

IoM proposals for a new disease taxonomy

Cancer clinical genomics

The Human Disease Ontology

The Human Phenotype Ontology

Wednesday, Mar 13 Afternoon: Public Session in Natcher

13:00-15:00

Barry Smith (Chair)

With presentations by

Cathy Wu (?Ontology work on actionable mutations​)​

Mark Musen (?BD2K and Biomedical Ontologies)

Warren Kibbe: TBD

Sponsors

• National Cancer Institute Center for Biomedical Informatics and Information Technology (CBIIT)
• National Center for Biomedical Ontology (NCBO)
• National Center for Ontological Research (NCOR)
• Center for Expanded Data Annotation and Retrieval ([CEDAR)

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Participants

will include:

• Evan Bolton (NIH / NLM / NCBI)
• Mathias Brochhausen (Biomedical Informatics, University of Arkansas for Medical Sciences)
• Gully Burns (Information Sciences Institute, University of Southern California)
• Sherri de Coronado (National Cancer Institute)
• Lindsay Cowell (UT Southwestern Medical Center)
• Peter Elkin (Department of Biomedical Informatics, University at Buffalo)
• Gilberto Fragoso (National Cancer Institute)
• Gang Fu (NIH / NLM / NCBI)
• Ilya Goldberg (Image Informatics and Computational Biology Unit, National Institute on Aging)
• Metin Gurcan (College of Medicine, Ohio State University)
• Warren Kibbe (National Cancer Institute / Disease Ontology)
• Raja Mazumder (Georgetown University / Protein Information Resource)
• Elvira Mitraka (University of Maryland, Baltimore)
• Susan Mockus, Jackson Laboratory for Genomic Medicine, Farmington, CT)
• Mark Musen (Stanford / National Center for Biomedical Ontology and Center for Expanded Data Annotation and Retrieval)
• Darren Natale (Georgetown University / Protein Ontology Consortium)
• Lynn Schriml (University of Maryland, Baltimore / Disease Ontology)
• Barry Smith (Buffalo / Open Biomedical Ontologies Foundry)
• John Tomaszewski (Pathology and Anatomical Sciences, Buffalo)
• Cathy Wu (Delaware / Protein Ontology)
• Wenjin J. Zheng (Center for Computational Biomedicine, University of Texas Health Science Center at Houston)