How Bio Ontologies Enable Open Science

How Bio-Ontologies enable Open Science Nigam Shah [email_address]

Ontologies By Pedro Beltrão

Key Points
Open science requires structured content.
Structured content acquisition runs into a curation bottleneck.
And “controlled manual curation” will not scale
For “open science” to really take off:
collaborative curation platforms are going to be necessary and,
(semi-)automation of curation is going to be necessary.
Researchers need to exactly identify what is being mentioned/discussed.
NCBO provides services that support these needs

Currently, the main use of ontologies is for making sense of high throughput data.
There are other uses of course, see Biomedical Ontologies: A functional perspective, Rubin et al, Briefings in Bioinformatics, Dec 2007, Vol 9:1 75-90

How does ontology help?
An ontology provides a organizing framework for creating “abstractions” of the high throughput data
The simplest ontologies (i.e. terminologies, controlled vocabularies) provide the most bang-for-the-buck
Gene Ontology (GO) is the prime example
More structured ontologies – such as those that represent pathways and more higher order biological concepts – still have to demonstrate real utility.

Ontologies and content acquisition
First start naming ‘things’
Then name ‘relationships’
Then comes the ‘logic of combining simple relationships’
… realization that all this “structure” is hard to create manually and manual curation will not scale … lots of dead projects.
Leads to new found love for text-mining!

Emerging trends in content acquisition
Increased Structure (in curation and annotations)
Collaborative curation platforms
Knewco
SWAN
CBioC
…
Integration of Text-mining in curation
Finding entities
BioLit by Phil Bourne’s group
Finding relations … facts.
Larry Hunter’s group
Biolink papers
EBI-MED

Increasing Structure
Until now the predominant use of ontologies is as a vocabulary to describe data … minimal structure in the descriptions.
Precise capture of biomedical knowledge in structured form is now considered essential
Hits the manual curation bottleneck.
WA Baumgartner Jr. et al, Manual curation is not sufficient for annotation of genomic databases. Bioinformatics 2007 23(13):i41-i48. Presented at ISMB 2007

Knewco: Concept Web and Wikiprofesional

The SWAN discourse ontology Ciccarese P, Wu E, Clark T (2007) 'An Overview of the SWAN 1.0 Ontology of Scientific Discourse‘ at the 16th International World Wide Web Conference Banff, Canada. May 8-12, 2007.

The SWAN Team and papers
Harvard/MGH : Paolo Ciccarese, Marco Ocana, Tim Clark
Alzforum : Elizabeth Wu, Gwen Wong, June Kinoshita www.alzforum.org
Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital [1] Gao Y, Kinoshita J, Wu E, Miller E, Lee R, Seaborne A, Cayzer S, Clark T (2006) ‘ SWAN: A Distributed Knowledge Infrastructure for Alzheimer Disease Research’. Journal of Web Semantics 4(3). [2] Ciccarese P, Wu E, Clark T (2007) 'An Overview of the SWAN 1.0 Ontology of Scientific Discourse'. 16th International World Wide Web Conference (WWW2007). Banff, Canada. May 8-12, 2007. [3] Clark T and Kinoshita J (2007) 'Alzforum and SWAN: The Present and Future of Scientific Web Communities'. Briefings in Bioinformatics 8(3). [4] Ciccarese, P, Wu E, Kinoshita J, Wong G, Ocana M, Ruttenberg A and Clark T (submitted for publication 9/4/2007) 'The SWAN Ontology of Scientific Discourse'. Photo not available

Integration of Text-mining + Curation
Text mining works better if it uses appropriate ontologies.
“ Model” mismatch b/w needs of text mining and needs of KB builders.
Text mining might work much better if:
It works in a loop with a curator
It leverages the wisdom of the masses

Integration of Text-mining + Curation

Quick recap
Use of ontologies in collaborative curation and content acquistion is not wide-spread; possibly because of:
Lack of a one stop shop for bio-ontologies
Lack of tools to use ontologies for annotation
Manual  will not scale
Automatic  can it be ‘good enough’?
Lack of a sustainable mechanism to create ontology based annotations

NCBO’s efforts
The key ingredients needed for collaborative curation platforms to succeed:
Proper use of bioontologies (just enough ontology!)
Appropriate use of Natural Language Processing in the curation workflow.
NCBO has created web-services that allow use of ontologies in collaborative platforms http://bioontology.org/tools.html

NCBO ontology services Base URL: http://rest.bioontology.org/rest Documentation: www.bioontology.org/wiki/index.php/NCBO_REST_services Description REST URL List all ontologies ./ontologies Find a specific ontology ./ontologies/{ontology version id} Download ontology file ./ontologies/download/{ontology version id} Get versions of an ontology ./ontologies/version/{ontology id} Get concept ./concepts/{ontology version id}/{concept id} Search for concepts ./search/concepts/{query}?ontologies={ids} Get latest version of an ontology ./virtual/{ontology_id} Get concept for latest ontology version ./virtual/{ontology id}/{concept id} List all ontology categories ./categories

NCBO annotation services
Open Biomedical Annotator (OBA) web service
To automatically process textual metadata to recognize relevant ontology concepts and return the terms as annotations
Open Biomedical Resource (OBR) index
To index the contents of a few biomedical resources with the biomedical concepts to which they relate … and allow programmatic access to the indexed data.
URL: http://obs.bioontology.org

ANNOTATOR SERVICE
Using Ontologies to Annotate Your Data

Annotator: The Basic Idea
Process textual metadata to automatically tag text with as many ontology terms as possible.

Annotator: Usage
Give your text as input
Select your parameters (ontologies to use, semantic type to filter, semantic expansion…)
Get your results… in text, tab-delimited, XML, or OWL
Paper in AMIA STB 09

DATA SERVICE
Using Ontologies to Access and Analyze Public Data

Open Biomedical Resources index
The index can be used for:
Search (next few slides)
Data mining (Paper in AMIA STB 08 on mining relationships b/w drugs, diseases and genes from Medline)

Example

NCBO services Ontology services (OBS) UMLS services BioPortal services Data service (OBR) Annotation service (OBA) Users UCSF Laboratree CollabRx PharmGKB, JAX HGMD Users BioPortal UI PDB/PLoS I2B2 NextBio IO informatics Users “ Resources” tab Knewco IO informatics

Uses of NCBO services
For programmatic access to latest versions of ontologies
For concept recognition from text
For annotation
For accelerating curation
For data aggregation and summarization

BioLit web resource: automated recognition of ontology terms and database IDs after publication http://biolit.ucsd.edu

Automated recognition of ontology terms and database IDs before publication with manual curation by author Word 2007 add-in

Annotation: UCSF
The task is to decide which trial is relevant for a particular patient.
Use the annotator service to map concepts in eligibility rules to UMLS CUIs
Use the annotations from the OBR index to create tag clouds in CTExplorer.

Annotation: Laboratree

Annotation: CollabRx caTissue/TIES Specimen Banking Specimen management is based on ontologies developed by NCI Ontology-based integration to create a virtual specimen bank

Curation: JAX, UCHSC, PDB/PLoS
JAX – Use concepts recognized in the abstracts of publications to triage papers for curation.
UCSHC – Wrap our annotator as a UIMA component and compare performance on full text
PDB/PLoS – BioLit and Word-plugin

Ontology Access: I2B2
Needs a “source” for ontologies in their ontology cell
Using our services, we export BioPortal Ontologies to the I2B2 format.

Ontology Access: IO-informatics

Ontology Access: NextBio
“ Our collaboration with NCBO on adopting public biomedical ontologies throughout NextBio enabled us to create a platform dealing with heterogeneous biological data. These ontology-based search capabilities have resulted in a rapid adoption of NextBio by over 100,000 researchers around the world since our public debut in May of 2008”.

Data Summarization: PharmGKB 1. CYP2C9 , 2. VKORC1 , 3. CYP2A6 , etc. 1. Hemorrhage , 2. Venous Thrombosis , etc. 1. warfarin , 2. coumarin , 3. phenoprocoumon , etc. 34 scored annotations: 5 scored annotations: 20 scored annotations:

Data Summarization: Knewco

Data Summarization: HGMD
Use the disease hierarchy from SNOMED-CT to compute “enrichment” of mutation types in particular types of diseases
… playing the GO-based microarray analysis game for disease mutations

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How Bio Ontologies Enable Open Science

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