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NCBO

Ontologies

Challenges

Diamond

Jambalaya

Interactive Views for Navigating Ontologies and Data at the National Center for Biomedical Ontology

MargaretMargaret Anne StoreyAnne Storey

Clinical Trials

Summary/Future

CogZ

Jambalaya MargaretMargaret--Anne StoreyAnne Storey

University of VictoriaUniversity of Victoria

NCBO

Ontologies

Challenges

Diamond

Jambalaya

Acknowledgements:

Chris Callendar, Tricia d’Entremont, Sean Falconer and Maleh Hernandez –The Chisel Group, UVic

Mark Musen, Principle Investigator NCBO –

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CSER Meeting October 2006 the CHISEL group, University of Victoria

Mark Musen, Principle Investigator NCBO Stanford University

Ida Sim, Driving Biological Project Lead, San Francisco University

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OverviewPart 1:

• National Center for Biomedical Ontology

• What are ontologies and why use them? – Key challenges

Part 2:

• Our projects

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the CHISEL group, University of Victoria

• Our projects– Degree of interest visualizations with Diamond

– Flexible and generic visualizations for Jambalaya and BioPortal

– Visualizing ontology alignments: CogZ

– Exploring clinical trials

• Future research directions

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National Center for Biomedical Ontology

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Goal: develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in both human readable and machine-processable form

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• Stanford: Tools for ontology alignment, indexing, and management

• Lawrence–Berkeley Labs: Tools to use ontologies for data annotation

• Mayo Clinic: Tools for accessing large controlled terminologies

• Victoria: Tools for ontology visualization

Participants in NCBO

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gy• University at Buffalo: Dissemination of best

practices for ontology engineering

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NCBO Driving Biological Projects

• Flybase (Cambridge UK)

• ZebraFish (ZFin project, Oregon)

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• HIV/AIDS Clinical Trial Bank(University of California at San Francisco)

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What is an Ontology?

• The study of being

• A discipline co-opted by computer science to enable the explicit specification of– Entities

– Properties and attributes of entities

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– Relations between entities

• A theory that provides a common vocabulary for an application domain

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Why develop an ontology?

• To share a common understanding of the entities in a given domain– among people

– among software agents

– between people and software

• To enable reuse of data and information

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– avoid re-invention of the wheel

– introduce standards to allow interoperability and automatic reasoning

• To create communities of researchers

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Ontologies are pervasive…

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A successful example: Foundational Model of Anatomy

• Long-term project at University of Washington to create a comprehensive ontology of human anatomy

• 72K concepts, 1.9M relationships

• One of the largest and best developed ontologies in biomedicine

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ontologies in biomedicine

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Top level of the foundational model of anatomy

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Ontologies are needed in electronic form

• Ontology contents can be processed and interpreted by computers

• Interactive tools can assist developers in ontology authoring

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Protégé Demo

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Ontologies enable large-scale science

Ontologies are currently at the center of two major activities in biomedical research

Structured representation of biomedicine:communities of researchers are creating and maintaining biomedical ontologies to represent different types of entities and relations to describe biomedicine (ontology

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relations to describe biomedicine (ontology content curation)

Annotation: biomedical experimentalists use ontologies to summarize and describe their results in a structured manner, enabling:

– Integration of their data with other researchers’ results

– Cross-species analyses, trial meta-analyses

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Challenges

But many ontology builders are not very good philosophers!

• Nearly always, ontologies are created to address pressing practical needs

• Most ontologies are relatively small, built and refined by small groups

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• Success rests on individual artisans, rather than on standard operating procedures

• Developers often lack training in formal modeling and languages used may be limited

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For example… The International Classification of Diseases

• An enumeration of diseases that forms the basis for all medical claims and reimbursements in the United States

• A “legacy” terminology that has its roots in 19th century epidemiology

• Created initially by biostatisticians with a

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pressing need to compare death statistics in different European countries

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The International Classification of Diseases: An excerpt…724 Unspecified disorders of the back724.0 Spinal stenosis, other than cervical724.00 Spinal stenosis, unspecified region724.01 Spinal stenosis, thoracic region724.02 Spinal stenosis, lumbar region724.09 Spinal stenosis, other724.1 Pain in thoracic spine724.2 Lumbago724.3 Sciatica

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724.4 Thoracic or lumbosacral neuritis724.5 Backache, unspecified724.6 Disorders of sacrum724.7 Disorders of coccyx724.70 Unspecified disorder of coccyx724.71 Hypermobility of coccyx724.71 Coccygodynia724.8 Other symptoms referable to back724.9 Other unspecified back disorders

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ICD9 (1977): A Handful of Codes forTraffic Accidents

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ICD10 (1999): 587 codes for such accidents

V31.22 Occupant of three-wheeled motor vehicle injured in collision with pedal cycle, person on outside of vehicle, nontraffic accident, while working for income

W65.40 Drowning and submersion while in bath-tub, street and highway, while engaged in sports activity

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engaged in sports activity

X35.44 Victim of volcanic eruption, street and highway, while resting, sleeping, eating or engaging in other vital activities

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ICD is used for lots of (too many?) things!

• ICD is used to code all patient encounters with the health-care system for:– Billing and reimbursement

– Institutional planning

– Disease surveillance and public health

Quality assurance

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– Quality assurance

– Economic modeling

• ICD was never intended to make the distinctions relevant to all these tasks!

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• Diseases would be organized with well-defined relationships

• Diseases would be associated with computer understandable definitions

• There would be well-defined rules for ensuring that descriptions are sensible

If real ontologists could build the ICD from scratch …

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ensuring that descriptions are sensible

• There would be well-defined mechanisms for creating user-specific views of the ICD

• There would be integration with bioinformatics resources that describe the molecular underpinnings of disease

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Summary of challenges

• Ontologies are used in many areas of science:– e.g. Gene Ontology -- new genes discovered daily

• Lack of agreement on meaning of familiar terms

• Ontologies need to be peer reviewed and updated through consensus

• Data annotations also have to be updated as

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• Data annotations also have to be updated as ontologies change!

• Reality is that many ontologies will have to coexist -- need mappings between the ontologies

• BUT! the ontologies, mappings and data annotations will also have to evolve…

• Issues of collaboration and trust…

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Goals of the Center Revisited

Unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies

Create new software tools so that scientists can use ontologies to annotate and analyze biomedical data

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data

Disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community

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NCBO plans to offer technologies

• To help build and extend ontologies• To locate ontologies and to relate them to

one another• To visualize relationships and to aid

understanding• To facilitate evaluation and annotation of

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To facilitate evaluation and annotation of ontologies

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Ontologies

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Key Technology: BioPortal

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Jambalaya Part 2…


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Our goal: Provide cognitive support for ontology developers and users through visual and intelligent user interfaces

Subprojects:

• Diamond: Degree of interest browsing

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Diamond: Degree of interest browsing

• Jambalaya: visualization in Protégé

• CogZ: Cognitive support for ontology alignment

• Visualizing Clinical Trials: – CTExplorer

– CTSearch (TagClouds)

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• DIaMOND—Degree of Interest Modeling for Ontology Navigation and Development

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for Ontology Navigation and Development

• http://www.thechiselgroup.org/diamond

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Motivation

• Navigating ontologies can be tedious… – Long scrolling lists, expanding/collapsing

nodes– Large number of irrelevant elements occlude

relevant information

• Users often don’t know where to start when

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navigating an unfamiliar ontology– Might appreciate “worn paths”

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DIaMOND -- Approach

• Applies principles of attention-reactive interfaces– Mechanism to calculate user’s degree of

interest (DOI)

– Dynamic display of information using the DOI

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• Goals– Draw user’s attention to interesting elements

– Reduce navigation overhead

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DIaMOND (plug-in)

• Three levels of interest– Interesting

• Has been interacted with such that the DOI value exceeds a threshold value

– Uninteresting• DOI value falls below the threshold value

L d k H b t

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– Landmark: Hub concept• Manually specified by user

• DOI value exceeds a threshold value

• Lightweight, easily reversible focus techniques

• Consistent with existing, familiar Protégé views

Diamond Demo

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Jambalaya

Highlighting and Filtering in the Class Browser

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Standard Highlighting Highlighting & Filtering

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Challenges

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Diamond’s Future Work

• Currently being evaluated in user studies

• Sharing DOI among users (many requests)

• Role and Task-based DOI calculations

• Use of instance data and annotated data to supplement DOI calculations

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• Integrate and evaluate Diamond approach in BioPortal

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Challenges

Diamond

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JambalayaVisualization of ontologies to support

navigation

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Challenges

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Visualization and Jambalaya

• Why are visualizations important? – Ontologies can be extremely large and complex,

difficult to navigate using just trees and lists– Visualizations help users understand ontologies

by showing overviews and hub concepts

• Other visualization approaches:– OntoViz, OWLViz, TGViz, ezOWL

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– IsaViz: visual RDF editor– Many graph-like visualization tools!

• What is Jambalaya?– Protégé Plug-in built on top of SHriMP

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Diamond

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Main Features in Jambalaya

• Classes and instances/individuals are represented by nodes in the graph

• Slots or properties are represented by arcs between nodes

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Jambalaya Demo

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Diamond

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Composite Arcs

• High level edges (lifted)

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• Inconsistencies “pop-out” in some views

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Different Views

– Nested View (Default)– Flat views

• Class Tree• Class & Instance Tree• Domain & Range (OWL)

– TreeMap View

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TreeMap View

– Query View

– Filmstrip

– Customized views

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FlexViz• Currently implemented as a Java Applet

• Integration of key ideas from Jambalaya with BioPortal

– In progress… determining required features

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FlexViz

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CogZ• Cognitive Support and Visualization

for Human-Guided Mapping Systems

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Ontology alignment

• Mapping terms from one ontology to another, preserving structural relationships

• Research emphasis mostly on the algorithm

Select Alignment Potential

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Select ontologies

Alignment algorithm

Potential mappings

Verified mappings

Mapping complete

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Ontology alignment continued

• Cognitively challenging

• The user must:– Make decisions about mapping candidates

– Supply custom mappings that were missed

Understand the domains and structure of both

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– Understand the domains and structure of both ontologies

– Remember decisions that were made

• Can be made easier through cognitive support

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Visualization plug-in for Prompt

• Prompt: framework to support ontology alignment

• Sean Falconer’s research:– Added a plug-in mechanism and

perspectives to Prompt to support other

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– CogZ: Integrating various visualizations and filters to support the user’s decision making process

CogZ Demo


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Future work in visualizing alignments

• Carry out studies to investigate the cognitive processes involved during ontology mapping

• Develop a model of cognitive support for mapping tasks

• Develop design principles based on this model• Develop and evaluate techniques to support

these design principles in mapping tools

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Jambalaya these design principles in mapping tools


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Visualizing Clinical Trials

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• Developing new ways of visualizing clinical trials data

• Many different classes of users: – Clinicians, trialists, researchers

• Current approach for accessing trials is

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through pubmed or clinicaltrials.gov or RCT Presenter (Trial Bank)

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Current Tools…

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• Developing new ways of visualizing clinical trials data

• Many different classes of users: – Clinicians, trialists, researchers

• Current approach for accessing trials is

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through pubmed or clinicaltrials.gov or RCT Presenter (Trial Bank)

• Currently striving for standard reporting procedures (ontology based)

• Visualization prototypes being developed for detailed and specific use cases

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Collaborative visualization

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CTExplorer Demo

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Limitations

• Use cases are highly specialized – trialist, researcher

• Requires a lot of information (which is currently not readily available)

• So what can we do with data that already preexists in ClinicalTrials.gov or pubMed?

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Searching ClinicalTrials.gov

• How many trials are there related to the concept of “liver cancer”??

•Cancer liver, 670• Carcinoma of Liver Cells, 344• Carcinoma, hepatocellular, 257• HCC 411

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Jambalaya • HCC, 411• Hepatocellular Carcinoma, 400• Hepatoma, 396

•Liver cancer, 683• Liver Cell Carcinoma, 432• Liver metastases, 333• Metastases liver, 329• Primary Carcinoma of Liver Cells, 320


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CTSearch

• Objective:– To improve the experience of seeking and comparing

clinical trials

• Approach:– Use multiple tag clouds to support dynamic filtering and

searching through clinical trials

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Tag Clouds for summarizing Web Search Results

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Tag Cloud visualization: CTSearch

• Browsing a larger set of trials through ClinicalTrials.gov

• Scenario• A trial designer wants to see all the

conditions and interventions for “liver cancer”. She wants to select one condition and see all the associated interventions (or i )

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CTSearchDemo

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January 2008 the CHISEL group, University of Victoria

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Condition

Phase

Study Design

Conditions

Select source

Search Trials

SearchAdvanced Search

PreferencesHeart Attack AND Seniors AND Los Angeles

34 Trials from Heart Attack AND Seniors AND Los Angeles

Integration in BioPortalTagSync approach (mock up – future work)

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Eligibility Criteria

Interventions

Size

Primary Outcomes

Secondary Outcomes

Overall Status

Interventions

1 A Study of the Safety and Efficacy of Nitric Oxide Reduction in Patients With Cardiogenic Shock After a Heart AttackBy Judith S. Hochman, M.D Funded By:INDUSTRYA Phase III International, Multi-Center, Prospective, Randomized, Double-Blind, Placebo-Controlled Study to Assess the Safety and Efficacy of Nitric Oxide Synthase Inhibition With Tilarginine Acetate Injection in Patients With Cardiogenic Shock Complicating Acute Myocardial Infarction, or the TRIUMPH Trial May 2005-January 2007 Terminated

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Summary

• Overview of NCBO

• Challenges – disparate ontologies, evolving, diversity of users/domains/tasks

• Exploring visualization approaches in Jambalaya for BioPortal mashup services

• Dealing with information overload and

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• Dealing with information overload and providing views that are useful (Diamond)

• CogZ: Cognitive support for ontology alignment (and other potential applications of concept comparison)

• Looking at specific areas for tool support –e.g. clinical trials, ICD 11 development

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Future research directions and themes

• Web based visualizations for a diverse and evolving user base and task set– Customizable and flexible views, mashups

• Cognitive support rather than visualization– Ontologies, annotations and mappings

• Collaborative support

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– Humans and software agents

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Input and suggestions would Input and suggestions would be greatly appreciated!be greatly appreciated!

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CSER Meeting October 2006 the CHISEL group, University of Victoria

g y ppg y pp

[email protected]@uvic.ca

Computer Human Interaction & Software Engineering LabDepartment of Computer Science, University of Victoria

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References and websites• NCBO:

– http://bioontology.org

– Rubin, D.L., S.E. Lewis, C.J. Mungall, S. Misra, M. Westerfield, M. Ashburner, I. Sim, C.G. Chute, H. Solbrig, M.-A. Storey, B. Smith, J. Day-Richter, N.F. Noy, and M.A. Musen, "The National Center for Biomedical Ontology: Advancing Biomedicine through Structured Organization of Scientific Knowledge", OMICS: A Journal of Integrative Biology, 10(2), 185-198, 2006.

• Jambalaya:

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• Jambalaya: – http://www.thechiselgroup.org/jambalaya

– Ernst, N. A., Storey, M.-A., and Allen, P. 2005. Cognitive support for ontology modeling. Int. J. Hum.-Comput. Stud. 62, 5 (May. 2005), 553-577.

• Diamond: – http://thechiselgroup.org/diamond

– d’Entremont, T. and Storey, M.-A., Using a Degree-of-Interest Model for Adaptive Visualizations in Protégé. Presented at the 9th Int. Protégé Conference, 2006.

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References and websites (2)• Visualization and Alignments:

– Prompt website: http://protege.stanford.edu/plugins/prompt/prompt.html

– S. M. Falconer, N. F. Noy, M. Storey. CogZ: Towards understanding the needs of cognitive support for ontology mapping. 2006. Stanford Medical Informatics Technical Report: SMI-2006-1225

• Clinical Trials:– Trial Bank, RCT Presenter: http://rctbank.ucsf.edu/

– CTeXplorer:

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– CTeXplorer: http://webhome.cs.uvic.ca/~chisel/projects/ctexplorer/flex/main.html

Acknowledgements:Chris Callendar, Tricia d’Entremont, Sean Falconer and Maleh

Hernandez – The Chisel Group, UVicMark Musen, Principle Investigator NCBO – Stanford University

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• Extra slides…

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CiteULike

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CiteUlike• Features:

– The user queries one or more tags and receives a list of users and groups that are interested in those tags

– Result: list of hyperlinks to articles, books or other sources of information.

– A tag cloud shows related tags for the user to use in further queries

– Results are based on the ‘union’ rather than the ‘intersection’ of tags

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Jambalaya than the intersection of tags. • Querying for ‘Hepatocelluar carcinoma’ will

bring all the resources tagged as ‘Hepatocelluar ’, ‘carcinoma’, or ‘Hepatocelluar carcinoma’. As a consequence, ‘breast-carcinoma’ will be part of the results.

– The tag cloud can be filtered. For example, filtering on ‘ca’ displays: ‘cancer’, ‘classification’, ‘communication’, and ‘education’. This only filters the cloud and not the list of results.


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