1 the future of biomedical informatics barry smith university at buffalo

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The Future of Biomedical Informatics

Barry SmithUniversity at Buffalohttp://ontology.buffalo.edu/smith

1. Biomedical Informatics Needs Data

2. The Problem of Local Coding Schemes

3. NIH Policies for Data Reusability and the Growth of Clinical Research Consortia

4. Is SNOMED the Solution?

5. The Gene Ontology

6. The OBO Foundry

7. The National Center for Biomedical Ontology

8. Ontology in Buffalo2

1.1. Biomedical Informatics Needs DataBiomedical Informatics Needs Data





6. The OBO Foundry



Biomedical Informatics Needs Data

• Four sides of the equation of translational medicine

• Biological data + clinical data

• Access + usability

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Problems of gaining access to clinical data

1. privacy, security, liability

2. incentives (value of data ...)

3. costs (training ...)

Making data (re-)usable through standards

• Standards provide– common structure and terminology– single data source for review (less redundant

data)

• Standards allow– use of common tools and techniques– common training– single validation of data

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Problems with standards

• Not all standards are of equal quality

• Once a bad standard is set in stone you are creating problems for your children and for your children’s children

• Standards, especially bad standards, have costs


2.2. The Problem of Local Coding SchemesThe Problem of Local Coding Schemes




6. The OBO Foundry


8. Ontology in Buffalo

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Multiple kinds of data in multiple kinds of silos

Lab / pathology data

Clinical trial data, including regulatory data

Electronic Health Record data

Patient histories (free text)

Medical imaging

Microarray data

Protein chip data

Flow cytometry

Mass spectrometry data

Genotype / SNP data

Mouse data, fly data, chicken data ...

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How to find your data?

How to find other people’s data?

How to reason with data when you find it?

How to work out what data you do not have?

How to understand the significance of your own data from 3 years ago?

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3.3. NIH Policies for Data Reusability and the NIH Policies for Data Reusability and the Growth of Clinical Research ConsortiaGrowth of Clinical Research Consortia



6. The OBO Foundry



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Sharing Research Data: Investigators submitting an NIH application seeking $500,000 or more in direct costs in any single year are expected to include a plan for data sharing or state why this is not possible (http://grants.nih.gov/grants/policy/data_sharing).

http://grants.nih.gov/grants/policy/data_sharing

http://grants.nih.gov/grants/policy/data_sharing

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Program Announcement (PA) Number: PAR-07-425

Title: Data Ontologies for Biomedical Research (R01)NIH Blueprint for Neuroscience Research, (http://neuroscienceblueprint.nih.gov/)National Cancer Institute (NCI), (http://www.cancer.gov)National Center for Research Resources (NCRR), (http://www.ncrr.nih.gov/)National Eye Institute (NEI), (http://www.nei.nih.gov/)National Heart Lung and Blood Institute (NHLBI), (http://http.nhlbi.nih.gov )National Human Genome Research Institute (NHGRI), (http://www.genome.gov)National Institute on Alcohol Abuse and Alcoholism (NIAAA), (http://www.niaaa.nih.gov/)National Institute of Biomedical Imaging and Bioengineering (NIBIB), (http://www.nibib.nih.gov/)National Institute of Child Health and Human Development (NICHD), (http://www.nichd.nih.gov/)National Institute on Drug Abuse (NIDA), (http://www.nida.nih.gov/)National Institute of Environmental Health Sciences (NIEHS), (http://www.niehs.nih.gov/)National Institute of General Medical Sciences (NIGMS), (http://www.nigms.nih.gov/)National Institute of Mental Health (NIMH), (http://www.nimh.nih.gov/)National Institute of Neurological Disorders and Stroke (NINDS), (http://www.ninds.nih.gov/)National Institute of Nursing Research (NINR), (http://www.ninr.nih.gov)

Release/Posted Date: August 3, 2007 Letters of Intent Receipt Date(s): December 18, 2007, August 18, 2008, December 22, 2009, and August 21, 2009 for the four separate receipt dates.

http://neuroscienceblueprint.nih.gov/

http://www.cancer.gov/

http://www.ncrr.nih.gov/

http://www.nei.nih.gov/

http://http.nhlbi.nih.gov/

http://www.genome.gov/

http://www.niaaa.nih.gov/

http://www.nibib.nih.gov/

http://www.nichd.nih.gov/

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Purpose. Optimal use of informatics tools and resources [data sets] depends upon explicit understandings of concepts related to the data upon which they compute. This is typically accomplished by a tool or resource adopting a formal controlled vocabulary and ontology ... that describes objects and the relationships between those objects in a formal way.

... this FOA solicits Research Project Grant (R01) applications from institutions/ organizations that propose to develop an ontology that will make it possible for software to understand how two or more existing data sets relate to each other.

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Currently, there is no convenient way to map the knowledge that is contained in one data set to that in another data set, primarily because of differences in language and structure. ... in some areas there are emerging standards. Examples include: •the Unified Medical Language System (UMLS), •the Gene Ontology, http://www.geneontology.org/, •the work supported by the caBIG project (https://cabig.nci.nih.gov/workspaces/VCDE/), •ontologies listed at the Open Biomedical Ontology web site (http://obo.sourceforge.net/).

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This FOA will support limited awards, each of which focuses on integrating information between two (or a few very closely related) data sets in a single subject domain. The hope is that the developed vocabularies and ontologies will serve as nucleation points for other researchers in the area to build upon by adopting and extending the vocabularies and ontologies developed under this FOA.

Applicants are expected to identify and adopt emerging standards (such as those listed above) whenever possible. Applicants are also strongly encouraged to federate their data under appropriate infrastructures when possible. One potential infrastructure is provided by the Biomedical Informatics Research Network (http://www.nbirn.net ). The caBIG infrastructure (http://cabig.cancer.gov ) is another well established infrastructure that researchers should consider.

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NIH anticipates that once important data sets in a topical area have been unified that others in that area will adopt the emerging standard.

The nucleation points should be able to interact with each other, e.g. through the use of tools that are made freely available to the research community, such as those created by the National Center for Biomedical Ontology (NCBO) (http://bioontology.org/) or by caBIG

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Another determinate of ontology acceptance is the degree to which the ontology conforms to best practices governing ontology design and construction.

Criteria have been developed, and are undergoing empirical validation, by the Vocabulary and Common Data Element Work Group of caBIG. Other criteria have been specified by the OBO Foundry (http://obofoundry.org/ ).

In this FOA, the applicant should specify the criteria with which the ontology will conform and the reasons that those criteria are relevant to the data sets being integrated by the proposed ontology.

Growth of Clinical and Translational Research Consortia

Examples:

• PharmGKB

• caBIG

• BIRN – Biomedical Informatics Research Network– BIRN Ontology Task Force

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4.4. Is SNOMED the Solution?Is SNOMED the Solution?


6. The OBO Foundry



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http://ontology.buffalo.edu/smith

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medical records

SNOMED codes

The Systematized Nomenclature of Medicine

• built by College of American Pathologists

• now maintained by International Health Terminology Standards Development Organisation

• access via Virginia Tech SNOMED CT® Browser http://snomed.vetmed.vt.edu/

• (semi-) Open Source

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SNOMED often includes non-perspicuous terms

FullySpecifiedName:

Coordination observable (observable entity)

FullySpecifiedName:

Coordination (observable entity)

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and more:

Self-control behavior: aggression (observable entity)

Physical activity target light exercise (finding)

is a type of physical activity finding (finding)

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odd bunchings

European is a ethnic group6

Other European in New Zealand (ethnic group) is a ethnic group

Mixed ethnic census group is a ethnic group

Flathead is a ethnic group

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Poor modular development• No clear strategy for improvement

• Difficult to use for coding

• A tax on world health information technology?

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SNOMED embraces only some of the multiple kinds of siloed data

Lab / pathology data

Electronic Health Record data

Patient histories

Clinical trial data, including regulatory data

Medical imaging

Microarray data

Protein chip data

Flow cytometry

Mass spectrometry data

Genotype / SNP data

Mouse data, fly data, chicken data ...

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5.5. The Gene Ontology The Gene Ontology

6. The OBO Foundry



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The Gene Ontology

Open Source

Cross-Species

Impressive annotation resource

Impressive policies for maintenance

MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDV

31sequence of X chromosome in baker’s yeast

How to do Biology across the Genome?

MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSELMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVVAGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEFRDLHQLRLCYEIYMADTPSVAVQAPPGYGKTELFHLPLIALASKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPVRNFIEEGYDGVTDLYVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQSQFGGITNLDFDAFEKAIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRSEDLSRGLSSYPTRMFNLIKEKSEVPLGHVHKIRKKVESQPEEALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVVWIHGKLGAAEKVSRTKEFVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDGGLCYLLSRKNSWAARNRKGELPPKEGCITEQVREFYGLESKKGKKGQHVGCCGSRTDLSADTVELIERMDRLAEKQATASMSIVALPSSFQESNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTASTNVRTNATTNASTNATTNASTNASTNATTNASTNATTNSSTNATTTASTNVRTSATTTASINVRTSATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSATTTASINVRTSATTTESTNSSTSATTTASINVRTSATTTKSINSSTNATTTESTNSNTNATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTNSNTSATTTESTNASAKEDANKDGNAEDNRFHPVTDINKESYKRKGSQMVLLERKKLKAQFPNTSENMNVLQFLGFRSDEIKHLFLYGIDIYFCPEGVFTQYGLCKGCQKMFELCVCWAGQKVSYRRIAWEALAVERMLRNDEEYKEYLEDIEPYHGDPVGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTRGKQGSQVFRMSGRQIKELYFKVWSNLRESKTEVLQYFLNWDEKKCQEEWEAKDDTVVVEALEKGGVFQRLRSMTSAGLQGPQYVKLQFSRHHRQLRSRYELSLGMHLRDQIALGVTPSKVPHWTAFLSMLIGLFYNKTFRQKLEYLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCRDSSREVGE

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what cellular component?

what molecular function?

what biological process?

A strategy for translational medicine

Sjöblöm T, et al. analyzed 13,023 genes in 11 breast and 11 colorectal cancers

using functional information captured by GO for given gene product types identified 189 as being mutated at significant frequency and thus as providing targets for diagnostic and therapeutic intervention. Science. 2006 Oct 13;314(5797):268-74.

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GO widely used

Sjöblöm T, et al. analyzed 13,023 genes in 11 breast and 11 colorectal cancers

using functional information captured by GO for given gene product types identified189 as being mutated at significant frequencies and thus as providing targets for diagnostic and therapeutic intervention.

Science. 2006 Oct 13;314(5797):268-74.

http://

ontologist.com

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Benefits of GO

1. links people to data

2. links data together

• across species (human, mouse, yeast, fly ...)

• across granularities (molecule, cell, organ, organism, population)

3. links medicine to biological science






6.6. The OBO FoundryThe OBO Foundry



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a shared portal for (so far) 58 ontologies (low regimentation)

http://obo.sourceforge.net NCBO BioPortal

2003

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Ontology Scope URL Custodians

Cell Ontology (CL)

cell types from prokaryotes to mammals

obo.sourceforge.net/cgi-

bin/detail.cgi?cell

Jonathan Bard, Michael Ashburner, Oliver Hofman

Chemical Entities of Bio-

logical Interest (ChEBI)

molecular entities ebi.ac.uk/chebiPaula Dematos,Rafael Alcantara

Common Anatomy Refer-

ence Ontology (CARO)

anatomical structures in human and model

organisms(under development)

Melissa Haendel, Terry Hayamizu, Cornelius

Rosse, David Sutherland,

Foundational Model of Anatomy (FMA)

structure of the human body

fma.biostr.washington.

edu

JLV Mejino Jr.,Cornelius Rosse

Functional Genomics Investigation

Ontology (FuGO)

design, protocol, data instrumentation, and

analysisfugo.sf.net FuGO Working Group

Gene Ontology (GO)

cellular components, molecular functions, biological processes

www.geneontology.org

Gene Ontology Consortium

Phenotypic Quality Ontology

(PaTO)

qualities of anatomical structures

obo.sourceforge.net/cgi

-bin/ detail.cgi?attribute_and_value

Michael Ashburner, Suzanna

Lewis, Georgios Gkoutos

Protein Ontology (PrO)

protein types and modifications

(under development)Protein Ontology

Consortium

Relation Ontology (RO)

relationsobo.sf.net/

relationshipBarry Smith, Chris

Mungall

RNA Ontology(RnaO)

three-dimensional RNA structures

(under development) RNA Ontology Consortium

Sequence Ontology(SO)

properties and features of nucleic sequences

song.sf.net Karen Eilbeck

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RELATION TO TIME

GRANULARITY

CONTINUANT OCCURRENT

INDEPENDENT DEPENDENT

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity(FMA, CARO)

OrganFunction

(FMP, CPRO) Phenotypic

Quality(PaTO)

Biological Process

(GO)CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Compone

nt(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule

(ChEBI, SO,RnaO, PrO)

Molecular Function(GO)

Molecular Process

(GO)

Building out from the original GO

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OBO Foundry Coordinators

LewisBerkeley

AshburnerCambridge

SmithBuffalo

MungallBerkeley

The goal

all biological (biomedical) research data should cumulate to form a single, algorithmically processible, whole

http://obofoundry.org

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CRITERIA

The ontology is open and available to be used by all.

The ontology is in, or can be instantiated in, a common formal language.

The developers of the ontology agree in advance to collaborate with developers of other OBO Foundry ontology where domains overlap.

FOUNDRY CRITERIA

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CRITERIA UPDATE: The developers of each ontology

commit to its maintenance in light of scientific advance, and to soliciting community feedback for its improvement.

ORTHOGONALITY: They commit to working with other Foundry members to ensure that, for any particular domain, there is community convergence on a single controlled vocabulary.

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OBO Foundry is serving as a benchmark for improvements in discipline-focused terminology resources

yielding callibration of existing terminologies and data resources and alignment of different views

Consequences

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Mature OBO Foundry ontologies (now undergoing reform)

Cell Ontology (CL)Chemical Entities of Biological Interest (ChEBI)Foundational Model of Anatomy (FMA)Gene Ontology (GO)Phenotypic Quality Ontology (PaTO)Relation Ontology (RO)Sequence Ontology (SO)

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Ontologies being built to satisfy Foundry principles ab initio

Common Anatomy Reference Ontology (CARO)Ontology for Biomedical Investigations (OBI)Protein Ontology (PRO)RNA Ontology (RnaO)Subcellular Anatomy Ontology (SAO)

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Ontologies in planning phaseBiobank/Biorepository Ontology (BrO, part of OBI)Environment Ontology (EnvO) Immunology Ontology (ImmunO)Infectious Disease Ontology (IDO)






6. The OBO Foundry

7.7. The National Center for Biomedical The National Center for Biomedical OntologyOntology


NCBO

National Center for Biomedical Ontology (NIH Roadmap Center)

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• Stanford Medical Informatics• University of San Francisco Medical Center• Berkeley Drosophila Genome Project• Cambridge University Department of Genetics• The Mayo Clinic• University at Buffalo Department of Philosophy






6. The OBO Foundry


8.8. Ontology in BuffaloOntology in Buffalo52

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Ontology Research Group in CoE

Werner CeustersLouis GoldbergBarry SmithRobert ArpThomas BittnerMaureen DonnellyDavid KoepsellRon RudnickiShahid Manzoor

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Ontologies in BuffaloCommon Anatomy Reference Ontology (CARO)Environment Ontology (EnvO) Foundational Model of Anatomy (FMA)Infectious Disease Ontology (IDO)MS OntologyProtein Ontology (PRO)Relation Ontology (RO)

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Ontologies plannedICF OntologyFood Ontology Allergy OntologyVaccine OntologyOntology for Community-Based MedicinePsychiatry Ontology

1 the future of biomedical informatics barry smith university at buffalo

Documents

data reusability

data sharing

data ontologies

chicken data

peoples data

sharing research data

redundant data standards

multiple kinds of data