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NCBO Webinar: Oliver He, May 2 - Vaccine Ontology (VO) and its application in literature mining of vaccine-gene interaction networks

From: Trish Whetzel <plwhetzel@gmail.com>
Date: Tue, 1 May 2012 10:23:02 -0700
Message-ID: <CAE4f=ng=YYN5WKszhHd86MqdrYBMFp3n98c6sCiLfLsUgkN2bg@mail.gmail.com>
To: HCLS <public-semweb-lifesci@w3.org>
The next NCBO Webinar will be presented May 2 by Oliver He from the
University of Michigan Medical School on "Vaccine Ontology (VO) and its
application in literature mining of vaccine-gene interaction networks" at
10:00am PT, Wednesday, May 2.  Below is information on how to join the
online meeting via WebEx and accompanying teleconference. For the full
schedule of the NCBO Webinar presentations see:

The Vaccine Ontology (VO, http://www.violinet.org/vaccineontology) is a
community-based biomedical ontology in the domain of vaccine and
vaccination. As of April 29, VO contains 5,578 ontology terms including
5,145 VO-specific terms, and the other terms are imported from over ten
existing ontologies. VO represents over 1,000 licensed vaccines and vaccine
candidates in clinical trials and in research. These vaccines have been
developed against infections of various bacterial, viral, and parasitic
pathogens as well as cancers in over 20 animal species.  VO also represents
all types of vaccine components (e.g., vaccine antigens and adjuvants) and
vaccine preparations. In addition, various vaccine-induced immune responses
and protection against specific disorders are being modeled in VO. We have
developed VO-based natural language processing (NLP) approaches to retrieve
and analyze vaccine-specific interaction networks of host or pathogen
genes. Our use case studies demonstrated: (1) VO and centrality-based
literature mining dramatically increased the identification of
interferon-gamma (IFNG) and vaccine-associated human genes and gene
interactions; (2) a NLP literature mining method based on VO and the
Interaction Network Ontology (INO) allows the detection of significantly
over- and under-represented gene-gene interactions in the IFNG network and
IFNG-vaccine subnetwork; and (3) VO-based literature mining provided better
performance than the MeSH-based PubMed approach in retrieving the
interactions between vaccines and genes from Brucella (a bacterial
pathogen). Novel scientific hypotheses have been generated through these
ontology-based literature mining studies.

Yongqun “Oliver” He is an associate professor in the Unit for Laboratory
Animal Medicine, Department of Microbiology, Center for Computational
Medicine and Bioinformatics, and Comprehensive Cancer Center of the
University of Michigan Medical School, Ann Arbor, MI, USA. He has initiated
and led the development of several biomedical ontologies, including the
community-based Vaccine Ontology (VO;
http://www.violinet.org/vaccineontology) and Ontology of Adverse Events
(OAE; http://www.oae-ontology.org). His group, through collaborations with
others, has developed several ontology-oriented software programs including
OntoFox (http://ontofox.hegroup.org) for importing and reusing individual
ontology terms and Ontobee (http://www.ontobee.org) as a linked RDF data
server for ontology term information retrieval and display. His group has
also developed and applied ontology-based literature mining tools to study
biological pathways with an emphasis on vaccine-associated gene interaction
networks among host and pathogen genes.

To start or join the online meeting
Go to
Meeting Password: ncbo

Audio conference information
To receive a call back, provide your phone number when you join the
meeting, or call the number below and enter the access code.
Call-in toll number (US/Canada): 1-650-429-3300
Global call-in numbers:

Access code:925 343 903

Trish Whetzel, PhD
Outreach Coordinator
The National Center for Biomedical Ontology
Ph: 650-721-2378

"Like" NCBO on Facebook: http://on.fb.me/bioontology

Follow NCBO on Twitter: http://twitter.com/#!/bioontology

Join in Discussions on LinkedIn: http://linkd.in/ncbo-group
Received on Tuesday, 1 May 2012 17:23:32 UTC

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