This document provides a summary of a seminar presentation on bio-ontology and its application in bioinformatics. It discusses key topics like the goals and elements of ontology, applications of ontology including in bioinformatics, importance of bioinformatics, need for ontology in bioinformatics, types of bioinformatics ontologies and relations used in cancer ontologies. It also summarizes the growth of bio-ontology papers over time, top ontologies in different biology domains, limitations and future prospects.

1. Bio-ontology : A case study
Seminar 3
Presented by: Anwesha Bhattacharya
3rd Semester, 2013-2015
MSLIS, DRTC
Seminar coordinator: Dr. Biswanath Dutta

2. Ontology
Goals of Ontology
Application of ontologies
Bioinformatics
Importance of Bioinformatics
Need of ontology in bioinformatics
Bioinformatics taxonomy
Library of bio-ontologies
Relations used in Cancer ontologies
Types of relations for disease ontology
Limitations
Conclusion & future prospects
CONTENTS:

4. Elements of ontology
An ontology is most often conceptualized as
comprising three main elements:
(1) a set of knowledge objects;
(2) a set of relations that form associations
(relationships) between the knowledge objects;
(3) a set of axioms that provides rules and
constraints for the relationships (e.g. if A is next to
B, then B is next to A).

6. Applications of Ontology

7. General applications
• Communication
– Between people (may be informal)
– Between agents (formal ontologies)
• Inter-operability
• Representing and storing data (e.g., DB schema)
• To analyze domain knowledge
• Knowledge sharing within and between domains
• To make domain assumptions explicit
• To share common understanding of the structure of information among people or software
agents.
• Classification and organization of data resources
• Establishing contacts
• Systems Engineering Benefits:
– Re-Usability

8. Bioinformatics

9. Bioinformatics
 Bioinformatics is the application of information
technology to the field of biology.
 The term Bioinformatics was coined by Pauline
Hogweg in 1979 for the study of informatics
processes in biotic systems.
 Bioinformatics is an interdisciplinary field that
develops and improves on methods for storing,
retrieving, organizing and analyzing biological data.

10. Relation b/w ontologies, biology, computer science and philosophy
Source: Schulze-Kremer, S. (2002). Ontologies for molecular biology and bioinformatics. N Silico biology 2, 0017.-

11. Importance of Bioinformatics

12. Why Bioinformatics?
Bioinformatics techniques such as image and signal processing allow extraction of useful
results from large amounts of raw data in the field of biology.
In the field of genetics and genomics, it aids in sequencing and annotating genomes and
their observed mutations.
It plays a role in the textual mining of biological literature.
• Ultimate goals:
i) Uncover the wealth of biological information hidden in the mass of sequence,
structure, literature and other biological data.
ii) Obtain a clearer insight into the fundamental biology of organisms and use this
information to enhance the standard of life for mankind.

13. Why Bioinformatics? (contd...)
It plays a role in the analysis of gene and protein expression and regulation.
Development of biological and gene ontologies to organize and query biological data.
Aids in the simulation and modeling of DNA, RNA, and protein structures as well as
molecular interactions.
Analyze and catalogue the biological pathways.

14. Bioinformatics can be used in various fields, as given below:
• Molecular medicine
• Gene therapy
• Antibiotic resistance
• Drug development
• Biotechnology
• Forensic analysis of microbes
• Evolutionary studies
• Waste cleanup

15. Source: Stevens, R., Goble, C. A. and Bechhafer, S. (2000). Ontology-based knowledge representation. Briefing in Bioinformatics. Vol. 1(4) : 398-414.

17. Why ‘ontologies’ play an important role in
Bioinformatics?
• Create standards
• Interoperability
• Exploring large data sets – Use in
investigating gene function.
• Mapping knowledge domains – Creating
an ontology network that allows a user working in one
area to take advantage of knowledge from a related
area.

18. Growth of bio-ontology papers
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
0
50
100
150
200
250
300
350
400
450
Source: Numbers of articles on “bio-ontology/ies” in PubMed/MEDLINE as on 15.9.2014

19. Bio-ontology Timeline (1992-2006)

20. Bio-ontology timeline (2009-2012)
Column2
LogMap
Mery-B
Granatum
0
2
4
6
8
10
12
14
6
6
6
6

21. Bioinformatics
Anatomy
(45)
Human
Animal
Microbes
Plant
Health
(91)
Disease
Drug
Cell biology
(11)
Processes
Metabolism
DNA
repair
Structures
Cell wall
Cell
membrane
Nucleus
Mitochondria
Biochemistry
(44)
Biological
processes
Carbohydrates
Lipids
Proteins
Nucleic acids
Genomics &
Proteomics
(18)
Genetics
Protein
structure
Immunology
(6)
Classification of bioinformatics ontologies
Note: There are 168 other ontologies which are not included here due to some discrepancies

24. Library of bio-ontologies
Ontology name Number
Anatomy 45
Health 91
Cell biology 11
Biochemistry 44
Genomics & Proteomics 18
Immunology 6
Others 168
Total 383
Source: http://bioportal.bioontology.org/ontologies

25. Bio-ontology distribution
45
91
11
44
18 6
Anatomy
Health
Cell biology
Biochemistry
Genomics & Proteomics
Immunology

26. Ontologies on Health
51
31
9 Health
Others (e.g FHHO, IMMDIS, OPE, OVAE etc.)
Diseases
Drug
7
31
Cancer
Diseases

27. Why cancer research is important?

With cancer being a leading cause of death worldwide, it
seems obvious that it would be an important research
focus.

Cancer research is very important in the fight against
cancer.

Cancer research is crucial to improve the prevention,
detection and treatment of cancers.

Cancer research will benefit the next generation of
cancer patients, research is also extremely important for
cancer patients being treated today.

Cancer ontologies would aid in exploring new avenues
leading to contributions in cancer research.

28. Names of cancer ontologies Description
Neomark Oral Cancer Ontology, version 4
(NEOMARK4)
Ontology that describes the medical information necessary
for early detection of the oral cancer reoccurrence extracted
from the NeoMark Project.
Neomark Oral Cancer Ontology, version 3 Ontology that describes the medical information necessary
for early detection of the oral cancer reoccurrence extracted
from the NeoMark Project.
Cancer chemoprevention Ontology (CANCO) The Cancer Chemoprevention Ontology constitutes a
vocabulary that is able to describe and semantically
interconnect the different paradigms of the cancer
chemoprevention domain.
National Cancer Institute Thesaurus (NCIT) A vocabulary for clinical care, translational and basic
research, public information and administrative activities.
Cancer Research and Management ACGT
Master Ontology (ACGT-MO)
The intention of the ACGT Master Ontology (MO) is to
represent the domain of cancer research and management in
a computationally tractable manner.
Upper-Level Cancer Ontology (CANONT) Providing an upper-level ontology for cancer.
Breast Cancer Grading Ontology assigns a grade to a tumor starting from the 3 criteria of the
Next Generation Sequencing (NGS) for cancer diagnostics

29. Motivations: Cancer Ontologies
Name of the Projects Description
A Social Collaborative Working Space Semantically
Interlinking Biomedical Researchers, Knowledge and
Data for the Design and Execution of In-Silico Models
and Experiments in Cancer Chemoprevention.
Cancer Bench-to-Bedside
(CaB2B)
caB2B, or cancer Bench-to-Bedside, is a tool that
enables querying for cancer related information
hosted anywhere on caGrid. It allows for Web-based
queries, which can be stored for later re-use. caB2B
users can semantically search and retrieve
information from the NCBO Resource Index.
Cancer Genome Anatomy Project
(CGAP)
The NCI's Cancer Genome Anatomy Project sought to
determine the gene expression profiles of normal,
precancer, and cancer cells, leading eventually to
improved detection, diagnosis, and treatment for the
patient. Resources generated by the CGAP initiative
are available to the broad cancer community.

30. Motivation: Cancer ontologies (Contd…)
• Ontologies provide a powerful mechanism for making conceptual information
about cancer biology computationally available.
• Ontologies provide mechanism by which conceptual information can be attached
to the current flood of cancer data and thereby help turn data into useful
knowledge.
• Developing a standard vocabulary for cancer ontologies as per requirements.
• Due to the heterogeneous information of the cancer ontologies, it is important to
find out a homogeneity.
• Aids in mining various diseases, methods and treatments from biological text
literatures.

31. Relation used in different
cancer ontologies

32. Ontological relations & its utilities

Relationships (also known as relations) between objects in an ontology
specify how objects are related/associated to other objects.

Typically a relation is of a particular type (or class) that specifies in what
sense the object is related to the other object in the ontology.

Much of the power of ontologies comes from the ability to describe
relations. Together, the set of relations describes the semantics of the
domain.

We mainly study the binary relations b/w the objects (here may be
diseases, treatments, methods etc).

In this context the study of relations in the cancer ontologies would
enable in mining the various diseases, methods, treatments which are
yet to be extracted from different text literatures.

33. − Second Outline
Level
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Third Outline
Level
− Fourth Outline
Level
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Fifth
Outline
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Sixth
Outline
Level
• Seventh Outline
LevelClick to edit
Master text styles
National Cancer Institute Thesaurus
(NCIT)

34. Breast cancer grading ontology Neomark Oral Cancer Ontology, version 4

35. − Second Outline
Level

Third Outline
Level
− Fourth Outline
Level

Fifth
Outline
Level

Sixth
Outline
Level
• Seventh Outline
LevelClick to edit
Master text styles
Cancer Chemoprevention Ontology (CANCO)

36. Neomark Oral
Cancer Ontology,
version 4
Breast cancer
grading ontology
Cancer Chemoprevention
Ontology
National Cancer Institute Thesaurus
adjacent_to
contained_in
located_in
location_of
Deal with spacial
relations
has_anatomical_entity
has_gland
has_tissue
Deal with spacial
relations
containsOrgan
containsTarget
‘has disease location’
Deal with spacial relations
Disease_Has_Normal_Cell_Origin
Disease_Has_Normal_Tissue_Origin
Gene_Has_Physical_Location
Gene_Has_Chromosomal_Location
Deal with spacial relations
derives_from ‘contain molecule’
naturalVsSynthetic
Indicate whether a Source is Natural
or Synthetic
hasSource
Chemopreventive Agent with the
sources where it is available or from
where it originates
‘related to disease’ All relations starting with Disease
part_of
proper_part_of
improper_part_of
integral_part_of
part_of
containAssay
Study that a Bioassay is part of
part_of
proper_part_of
improper_part_of
integral_part_of
‘induce prevent’
‘interact with’
Chemical_Or_Drug_Affects_Abnormal_Cell
Chemical_Or_Drug_Affects_Cell_Type_Or_Tis
sue
Chemical_Or_Drug_Affects_Gene_Product
hasBiologicalMechanism Biological_Process
hasTarget
target of biological mechanism in
order to prevent cancer.
hasTarget

37. Types of relations for disease
ontology
Relations Relations Relations
associated patient inherence
initiator includes risk factor
parthood excludes transformation
origin affects spatial relation
abnormality effect participants
agent role constituent
treatment result

38. Discussions
• Listing of the relations for disease ontologies
is at a premature stage
• Whether the list is exhaustive/not is not
known. Needs in-depth research.
• Attempt to provide an abstraction of relations
which would aid in developing an upper level
ontology for diseases in general.
• No specific relations have been mentioned as
of now.

39. Limitations
• Time constraint
• Domain knowledge
• Study of 4 cancer ontologies
• All disease ontologies need to be considered
to build a common framework
• Vast periphery

40. Conclusions & Future prospects
• Biologically connected objects can be
explored.
• Study of relations can aid in discovering the
unexplored entities.
• Importance of studying the objects rather
than the value of the property of the objects.
• Initiation of work for building a common
framework for all the disease ontologies to be
built in future

41. References
• Schulze-Kremer, S. (2002). Ontologies for molecular biology and bioinformatics. N Silico biology 2, 0017.
• Stevens, R., Goble, C. A. and Bechhafer, S. (2000). Ontology-based knowledge representation. Briefing in Bioinformatics.
Vol. 1(4) : 398-414.
• http://www.bioinformatics.kmutt.ac.th/download/seminar/bif04/Alisa_PPT1.pdf
• http://informatics.sdsu.edu/bioinformatics/
• Bodenreider, Olivier and Stevens, Robert. (2006) Bio-ontologies: current trends and future directions.
• Karp, Peter D. (2000). An ontology for biological function based on molecular interactions.
• Jonathan B. L. Bard* and Seung Y. Rhee. (2004). ONTOLOGIES IN BIOLOGY: DESIGN, APPLICATIONS AND FUTURE
CHALLENGES. Nature reviews Genetics. Vol 5. p (213-222).
• http://www.w3.org/wiki/Semantic_Bioinformatics
• A Framework for Understanding and Classifying Ontology Applications, Mike Uschold & Robert Jasper
• http://www.cs.man.ac.uk/~horrocks/Teaching/cs646/Papers/uschold99.pdf
• http://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html

43. Thank you