The Role of Ontology in Big Cancer Data

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Date: May 12-13, 2015

Hotels

We have a block of rooms reserved near the NCI building which is our venue on Day 1 in:

Hilton Garden Inn Rockville-Gaithersburg

14975 Shady Grove Road

Rockville, MD

(240) 507-1900

The link for reservations is here. Cut-off date is April 13th.

For other hotels in the area, see this list.

Venue

  • Day 1 (10am-5pm) will be in NCI's Shady Grove building (9609 Medical Center Dr, Rockville, MD, 20850), in Room 2W-32/34.
  • Day 2 (9am-3pm) will be a public session in Balcony A, 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.

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

10:00-13:00

Barry Smith (Buffalo): The cancer research ontology space: An introduction

an introduction to existing ontology resources in the cancer domain, including NCI Thesaurus and the OBO Foundry; addressing opportunities and reasons for scepticism as concerns the use of ontologies in addressing cancer big data; topics to address will include the ontology of tissues, of tissue banks, and of image banks

Ilya Goldberg (NIA): The role of imaging ontologies in cancer big data

Metin Gurcan (Ohio State) and John Tomaszewski (University at Buffalo): How ontologies can help in addressing the big data challenges of pathology imaging

widespread availability of whole slide scanners has a transformative effect on pathology, resulting in data that is "big” not only in terms of size but also in richness of information. Computational algorithms have been developed to tap into this data both to assist pathologists and to improve diagnosis, prognosis and treatment. Now, however, there is an urgent need to provide a set of terms and formal definitions necessary to characterize both the histopathological images and the algorithms that operate on them. We will present in this light our on-going work to create an ontology for histopathological imaging.
Goals:

1. Describe the transformative role of digital pathology

2. Explain the big data challenges in clinical research and computational algorithms

3. Outline how ontologies can provide solutions to address the big data challenges of pathology imaging

13:00 Lunch

Session 2: Addressing cancer big data challenges with the Ontology for Biomedical Investigations (OBI)

14:00-17:00

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

tasks: address the challenge that cancer research is multidisciplinary and requires standard terminology from multiple domains.
briefly describe OBI and 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

address 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: Addressing Cancer Big Data Challenges

Wednesday, May 13 in Balcony A, Natcher, 9:00-12:00

9:00 Roles of Ontologies in Cancer Big Data

Olivier Bodenreider (NLM): SNOMED-CT

Lynn Schriml (University of Maryland Baltimore): A Human Disease Ontology (HDO) unified representation of cancer disease terms from COSMIC, ICGC, TCGA, IntOGen and UniProt

topics to be covered include:
1. the genetic view of disease in the Human Disease Ontology (mutation types, inheritance types, specific mutations, chromosome locations);
2. Institute of Medicine proposals for a new disease taxonomy resting on defining diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms, for example dividing lung cancers into subsets defined by driver mutations


10:00 Break

10:15 The Role of Ontology in Cancer Big Data Use Cases

Lindsay Cowell (UT Southwestern): HPV and cervical cancer data in Electronic Health Records -- A Big Data challenge

what types of data do we need to represent? HPV and cervical cancer and the Infectious Disease Ontology; challenges involved in keeping and using large collections of samples and of sample data

Raja Mazumder (George Washington University): The need for cancer disease ontology for pan-cancer data integration and analysis.

Susan Mockus (The Jackson Laboratory, Genomic Medicine)

Peter Elkin (University of Buffalo): Ontology-based cancer biomarker discovery

11:15 Discussion and Session Wrap Up

Ontology/vocabulary challenges, Use case challenges
Challenges, Needs, Action Item Solutions, short term steps, long term goals

12:00 Lunch

Public Session: Cancer Big Data to Knowledge

13:00-15:00

Barry Smith (Chair)

Philip E. Bourne (NIH / ADDS): The NIH Big Data Strategy

Cathy Wu (University of Delaware / PRO): Ontology and the Precision Medicine Initiative: The Role of OBO Foundry Ontologies in Protein-Centric Cancer Knowledge Network Discovery

Mark Musen (Stanford / NCBO): CEDAR: Making it Easier to Use Ontologies to Author Clinical Metadata

Warren Kibbe (NIH / NCI): 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)
  • Philip E. Bourne (NIH / ADDS)
  • 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)
  • Jingshan Huang (University of South Alabama / ITCR)
  • Rebecca Jacobson (University of Pittsburgh / ITCR)
  • Warren Kibbe (NIH / NCI / Disease Ontology)
  • Christopher ​Kinsinger (NIH/NCI)
  • Jerry Li (NIH / NCI)
  • 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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