The Role of Ontology in Big Cancer Data

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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

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.

Draft Partial Schedule

Session 1: Addressing Cancer Big Data Challenges through Imaging Ontologies

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

10:00 Ilya Goldberg (NIA) The Role of Imaging Ontologies in Cancer Big Data

11:00 Metin Gurcan (Ohio) and John Tomaszewski (Buffalo): How can ontologies help with the big data challenges of pathology imaging?

13:00 Lunch

Session 2: Addressing Cancer Big Data Challenges with the Ontology for Biomedical Investigations (OBI)

14:00

Chris 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.

Mathias Brochhausen (Arkansas): OBI-based integration of biobank data for cancer research

Session 3: Cancer Big Data and the Ontology of Disease

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

9:00 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

12:00 Lunch

Public Session

13:00-15:00, Wednesday, Mar 13 in Natcher

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)

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)


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