semantic (web) technologies for translational research in life sciences

Knowledge Enabled Information and Services Science

Semantic (Web) Technologies for Translational Research in Life Sciences

Ohio State University, June 16, 2011

Amit P. ShethOhio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)

[email protected]

Thanks to Kno.e.sis team (Satya, Priti, Rama, and Ajith);Collaborators at CTEGD UGA (Dr. Tarleton, Brent Weatherly),

NLM (Olivier Bodenreider), CCRC, UGA (Will York), NCBO/Stanford, CITAR/WSU

http://knoesis.wright.edu/amit/

http://www.knoesis.org/research/


Kno.e.sis: Ohio Center of Excellence in Knowledge-enabled Computing

mailto:[email protected]


Evolution of Web & Semantic Computing

Web of pages - text, manually created links

- extensive navigation

2007

1997

Web of databases - dynamically generated pages

- web query interfaces

Web of resources - data, service, data, mashups

Web of people

- social networks, user-created casual content

Sem

antic

Tec

hnol

ogy

Use

d

Keywords

Patterns

Objects

Situations,Events

Tech assimilated in life

Web 1.0

Web 2.0

Web 3.0

Web of Sensors, Devices/IoT- 40 billion sensors, 5 billion mobile connections


Outline

• Semantic Web – very brief intro

• Scenarios to demonstrate the applications and benefit of semantic web technologies– Health Care– Biomedical Research– Translational


Biomedical Informatics...

Medical Informatics Bioinformatics

Etiology PathogenesisClinical findingsDiagnosisPrognosisTreatment

GenomeTranscriptome

ProteomeMetabolome

Physiome...ome

Genbank

Uniprot

Pubmed

Clinical Trials.gov

...needs a connection

Biomedical Informatics

Hypothesis ValidationExperiment design

PredictionsPersonalized medicine

Semantic Web research aims atproviding this connection!

More advanced capabilities for search, integration, analysis, linking to new insights and discoveries!


Biochemistry

Molecular Biology

Cellular biology

Anatomy,

Physiology

Neuroscience

Cognitive Science,

Psychology

Health &

Performance

Data and Facts

Knowledge and Understanding

Decision Making, Insights, InnovationsHuman Performance

ACATATGGGTACTATTTACTATTCATGGGTACTATTTAT

GGCATATGGCGTACTATTCTAATCCTATATCCGTCTAATCTATTTACTATTATCTATTA

CTATACCTTTTGGGGAAAAAAATTCTATACCGTCTAAT

CCTATAAATCAAGCCG


Semantic Web standards @ W3C

• Semantic Web is built in a layered manner• Not everybody needs all the layers

Encoding characters : Unicode

Encoding structure: XML

Uniform metamodel: RDF + URI

Simple data models & taxonomies: RDF Schema

Rich ontologies: OWL

Queries: SPARQL, Rules: RIF

…

Semantic Web


Linked Data: Semantic Web “diluted”

• Achieve for data what Web did to documents• Relationship with the original Semantic Web vision: no AI,

no agents, no autonomy• Interoperability is still very important

– interoperability of formats– interoperability of semantics

• Enables interchange of large data sets– (thus very useful in, say, collaborative research)

• Semantic Web vision is largely predicated on the availability of data– Linked Data is a movement that gets us there

Thanks – Ora Lassila


Opportunity: exploiting clinical and biomedical data

text

Health Information

Services

Elsevier iConsult

Scientific Literature

PubMed300 Documents

Published Online each day

User-contributed Content (Informal)

GeneRifsWikiGene

NCBI Public Datasets

Genome, Protein DBs

new sequencesdaily

Laboratory Data

Lab tests, RTPCR,

Mass spec

Clinical Data

Personal health history

Search, browsing, complex query, integration, workflow, analysis, hypothesis validation, decision support.


Major Community Efforts

• W3C Semantic Web Health Care & Life Sciences Interest Group: http://www.w3.org/2001/sw/hcls/

• Clinical Observations Interoperability: EMR + Clinical Trials: http://esw.w3.org/HCLS/ClinicalObservationsInteroperability

• National Center for Biomedical Ontologies: http://bioportal.bioontology.org/

http://www.w3.org/2001/sw/hcls/

http://esw.w3.org/HCLS/ClinicalObservationsInteroperability

http://esw.w3.org/HCLS/ClinicalObservationsInteroperability

http://bioportal.bioontology.org/


Major SW Projects

• OpenPHACTS: A knowledge management project of the Innovative Medicines Initiative (IMI), a unique partnership between the European Community and the European Federation of Pharmaceutical Industries and Associations (EFPIA). http://www.openphacts.org/

• LarKC: develop the Large Knowledge Collider, a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. http://www.larkc.eu/

• NCBO: contribute to collaborative science and translational research. http://bioportal.bioontology.org/

http://www.imi.europa.eu/

http://www.efpia.org/Content/Default.asp

http://www.openphacts.org/


http://bioportal.bioontology.org/



• Ontology: Agreement with Common Vocabulary & Domain Knowledge; Schema + Knowledge base

• Semantic Annotation (meatadata Extraction): Manual, Semi-automatic (automatic with human verification), Automatic

• Semantic Computation: semantics enabled search, integration, complex queries, analysis (paths, subgraph), pattern finding, mining, inferencing, reasoning, hypothesis validation, discovery, visualization

Semantic Web Enablers and Techniques


Drug Ontology Hierarchy (showing is-a relationships)

owl:thing

prescription_drug

_ brand_na

me

brandname_unde

clared

brandname_comp

osite

prescription_drug

monograph_ix_cla

ss

cpnum_ group

prescription_drug

_ property

indication_

property

formulary_

property

non_drug_

reactant

interaction_proper

ty

property

formulary

brandname_indivi

dual

interaction_with_prescriptio

n_drug

interaction

indication

generic_ individua

l

prescription_drug_ generic

generic_ composit

e

interaction_ with_non_ drug_react

ant

interaction_with_monograph_ix_class


N-Glycosylation metabolic pathway

GNT-Iattaches GlcNAc at position 2

UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2 <=>

UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2

GNT-Vattaches GlcNAc at position 6

UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021

N-acetyl-glucosaminyl_transferase_VN-glycan_beta_GlcNAc_9N-glycan_alpha_man_4


Maturing capabilites and ongoing research

• Ontology Creation• Semantic Annotation & Text mining: Entity recognition,

Relationship extraction• Semantic Integration & Provenance:

– Integrating all types of data used in biomedical research: text, experimetal data, curated/structured/public and multimedia

– Semantic search, browsing, analysis

• Clinical and Scientific Workflows with semantic web services

• Semantic Exploration of scientific literature, Undiscovered public knowledge


Project 1: ASEMR

• Why: Improve Quality of Care and Decision Making without loss of Efficiency in active Cardiology practice.

• What: Use of semantic Web technologies for clinical decision support

• Where: Athens Heart Center & its partners and labs

• Status: In use continuously since 01/2006


Operational since January 2006

Details: http://knoesis.org/library/resource.php?id=00004

http://knoesis.org/library/resource.php?id=00004


Annotate ICD9s

Annotate Doctors

Lexical Annotation

Level 3 Drug

Interaction

Insurance Formular

y

Drug Allergy

Active Semantic EMR

Demo at: http://knoesis.org/library/demos/

http://knoesis.org/library/demos/


Project 2: Glycomics

• Why: To help in the treatment of certain kinds of cancer and Parkinson's Disease.

• What: Semantic Annotation of Experiment Data

• Where: Complex Carbohydrate Research Center, UGA

• Status: Research prototype in use– Workflow with Semantic Annotation of

Experimental Data already in use


N-Glycosylation Process (NGP)

Cell Culture

Glycoprotein Fraction

Glycopeptides Fraction

extract

Separation technique I

Glycopeptides Fraction

n*m

n

Signal integrationData correlation

Peptide Fraction

Peptide Fraction

ms data ms/ms data

ms peaklist ms/ms peaklist

Peptide listN-dimensional arrayGlycopeptide identificationand quantification

proteolysis

Separation technique II

PNGase

Mass spectrometry

Data reductionData reduction

Peptide identificationbinning

n

1


Storage

Standard FormatData

Raw Data

Filtered Data

Search Results

Final Output

Agent Agent Agent Agent Biological Sample Analysis

by MS/MS

Raw Data to

Standard Format

DataPre-

process

DB Search

(Mascot/Sequest)

Results Post-

process

(ProValt)

O I O I O I O I O

Biological Information

SemanticAnnotationApplications

Scientific workflow for proteome analysis


830.9570 194.9604 2

580.2985 0.3592

688.3214 0.2526

779.4759 38.4939

784.3607 21.7736

1543.7476 1.3822

1544.7595 2.9977

1562.8113 37.4790

1660.7776 476.5043

parent ion m/z

fragment ion m/z

ms/ms peaklist data

fragment ionabundance

parent ionabundance

parent ion charge

Semantic Annotation of Experimental Data

Mass Spectrometry (MS) Data


<ms-ms_peak_list><parameter

instrument=“micromass_QTOF_2_quadropole_time_of_flight_mass_spectrometer” mode=“ms-ms”/>

<parent_ion m-z=“830.9570” abundance=“194.9604” z=“2”/><fragment_ion m-z=“580.2985” abundance=“0.3592”/><fragment_ion m-z=“688.3214” abundance=“0.2526”/><fragment_ion m-z=“779.4759” abundance=“38.4939”/><fragment_ion m-z=“784.3607” abundance=“21.7736”/><fragment_ion m-z=“1543.7476” abundance=“1.3822”/><fragment_ion m-z=“1544.7595” abundance=“2.9977”/><fragment_ion m-z=“1562.8113” abundance=“37.4790”/><fragment_ion m-z=“1660.7776” abundance=“476.5043”/>

</ms-ms_peak_list>

OntologicalConcepts

Semantic Annotation of Experimental Data

Semantically Annotated MS Data


Project 3:

• Why: To associate genotype and phenotype information for knowledge discovery

• What: integrated data sources to run complex queries– Enriching data with ontologies for integration,

querying, and automation– Ontologies beyond vocabularies: the power of

relationships• Where: NCRR (NIH) • Status: Completed


Use data to test hypothesis

gene

GO

PubMed

Gene name

OMIM

Sequence

Interactions

Glycosyltransferase

Congenital muscular dystrophy

Link between glycosyltransferase activity and congenital muscular dystrophy?

Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07


In a Web pages world…

Congenital muscular dystrophy,type 1D

(GeneID: 9215)

has_associated_disease

has_molecular_function

Acetylglucosaminyl-transferase activity

Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07


With the semantically enhanced data

MIM:608840Muscular dystrophy,

congenital, type 1D

GO:0008375

has_associated_phenotype

has_molecular_function

EG:9215LARGE

acetylglucosaminyl-transferase

GO:0016757glycosyltransferase

GO:0008194isa

GO:0008375acetylglucosaminyl-

transferase

GO:0016758

From medinfo paper.Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07

SELECT DISTINCT ?t ?g ?d { ?t is_a GO:0016757 . ?g has molecular function ?t . ?g has_associated_phenotype ?b2 . ?b2 has_textual_description ?d .FILTER (?d, “muscular distrophy”, “i”) . FILTER (?d, “congenital”, “i”) }


Project 4: Nicotine Dependence

• Why: For understanding the genetic basis of nicotine dependence.

• What: Integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base.

• How: Semantic Web technologies (especially RDF, OWL, and SPARQL) support information integration and make it easy to create semantic mashups (semantically integrated resources).

• Where: NLM (NIH) • Status: Completed research


Motivation

• NIDA study on nicotine dependency• List of candidate genes in humans• Analysis objectives include:

o Find interactions between geneso Identification of active genes – maximum

number of pathwayso Identification of genes based on anatomical

locations• Requires integration of genome and biological

pathway information


Entrez Gene

ReactomeKEGG

HumanCyc

GeneOntology HomoloGene

Genome and pathway information integration

• pathway

• protein

• pmid

• pathway

• protein

• pmid

• pathway

• protein

• pmid

• GO ID • HomoloGene

ID


JBI


BioPAXontology

EntrezKnowledge

Model(EKoM)


Results: Gene Pathway network and Hub Genes involved with Nicotine Dependence


Project 5: T. cruzi SPSE

• Why: For Integrative Parasite Research to help expedite knowledge discovery

• What: Semantics and Services Enabled Problem Solving Environment (PSE) for Trypanosoma cruzi

• Where: Center for Tropical and Emerging Global Diseases (CTEGD), UGA

• Who: Kno.e.sis, UGA, NCBO (Stanford)• Status: Research prototype – in regular lab

use


Project Outline

• Data Sources Internal Lab Data

• Gene Knockout• Strain Creation• Microarray• Proteome

External Database• Ontological

Infrastructure Parasite Lifecycle Parasite Experiment

• Query processing Cuebee

• Results


Provenance in Parasite Research

*T.cruzi Semantic Problem Solving Environment Project, Courtesy of D.B. Weatherly and Flora Logan, Tarleton Lab, University of Georgia

SequenceExtraction

PlasmidConstruction

Transfection

DrugSelection

CellCloning

Gene Name

3‘ & 5’Region

Knockout Construct Plasmid

Drug Resistant Plasmid

Transfected Sample

Selected Sample

ClonedSample

T.Cruzi sample

Cloned Sample

Gene Name

?

Gene Knockout and Strain Creation*

Related Queries from Biologists• List all groups in the lab that used a

Target Region Plasmid?• Which researcher created a new

strain of the parasite (with ID = 66)?• An experiment was not successful –

has this experiment been conducted earlier? What were the results?

http://knoesis.wright.edu/trykipedia

http://knoesis.wright.edu/trykipedia


Research Accomplishments

• SPSE Integrated internal data with external databases, such as KEGG,

GO, and some datasets on TriTrypDB Developed semantic provenance framework and influence W3C

community SPSE supports complex biological queries that help find gene

knockout, drug and/or vaccination targets. For example:Show me proteins that are downregulated in the

epimastigote stage and exist in a single metabolic pathway.Give me the gene knockout summaries, both for plasmid

construction and strain creation, for all gene knockout targets that are 2-fold upregulated in amastigotes at the transcript level and that have orthologs in Leishmania but not in Trypanosoma brucei.


Knowledge driven query formulation

Complex queries can also include:- on-the-fly Web services execution to retrieve additional data- inference rules to make implicit knowledge explicit


Project 6: HPCO

• Why: collaborative knowledge exploration over scientific literature

• What: An up-to-date knowledge based literature search and exploration framework

• How: Using information extraction, conventional IR, and semantic web technologies for collaborative literature exploration

• Where: AFRL• Status: Completed research


Focused KB Work Flow (Use case: HPCO)

Doozer: Base Hierarchy from

WikipediaFocused

Pattern based extraction

HPC keywords

SenseLab Neuroscience

Ontologies

Initial KB Creation

NLM: Rule based BKR Triples

Knoesis: Parsing based NLP Triples

Meta Knowledgebase

PubMed Abstracts

Enrich Knowledge Base

Final Knowledge Base


Triple Extraction Approaches

• Open Extraction– No fixed number of predetermined entities and

predicates– At Knoesis – NLP (parsing and dependency

trees)• Supervised Extraction

– Predetermined set of entities and predicates– At Knoesis – Pattern based extraction to

connect entities in the base hierarchy using statistical techniques

– At NLM – NLP and rule based approaches


Mapping Triples to Base Hierarchy

• Entities in both subject and object must contain at least one concept from the hierarchy to be mapped to the KB

• Preliminary synonyms based on anchor labels and page redirects in Wikipedia

– Prolactostatin redirects to Dopamine

• Predicates (verbs) and entities are subjected to stemming using Wordnet


Scooner: Full Architecture


Scooner Features

• Knowledge-based browsing: Relations window, inverse relations, creating trails

• Persistent projects: Work bench, browsing history, comments, filtering

• Collaboration: comments, dashboard, exporting (sub)projects, importing projects


Scooner Screenshot


New Knowledge/hypothesis Example

• VIP Peptide – increases – Catecholamine Biosynthesis• Catecholamines – induce – β-adrenergic receptor activity• β-adrenergic receptors – are involved – fear conditioning

Three triples from different abstracts

New implicit knowledgeVIP Peptide – affects – fear conditioning

Caveat: Each triple above was observed in a different organism (cows, mice, humans), but still interesting hypothesis. Scooner’s contextual browsing makes this clear to the user.


Project 7: Drug Abuse

• Why: To study social trends in pharmaceutical opioid abuse

• What: – Describe drug user’s knowledge, attitudes, and

behaviors related to illicit use of OxyContin® – Describe temporal patterns of non-medical use of

OxyContin® tablets as discussed on Web-based forums • Where: CITAR (Center for Interventions, Treatment and

Addictions Research) at Wright State Univ.• Status: In-progress (Recently funded from NIDA)


Project 8: NMR

• Why: Streamline the NMR data processing tasks. Processing NMR experimental data is complex and time consuming.

• What: Providing biologists with tools to effectively process and manage Nuclear Magnetic Resonance (NMR) experimental data.

• How: Use Domain Specific Languages (DSL) to create scientist-friendly abstractions for complex statistical workflows. Use semantics based techniques to store and manage data.

• Where: Air Force Research Lab• Status: In progress


Motivation

NMR spectroscopy data is complex and require significant statistical processing before interpreting- Writing these processes is hard

- They have to run on many different computational platforms

- The data collected has to be shared among multiple parties

A simple NMR spectrum, highlighting peaks that

correspond to the presence of specific chemical compounds


A complex NMR spectrum, marked with chemical compound identifiers by human observers.


Project Outline

Identify fundamental operators required for common NMR processing tasks

Use a DSL to provide abstractions for the operators (named SCALE)

Build compilers to generate multiple, cloud-enabled applications


Real time Healthcare Information

• Matching medical requirements with availability of medical resources (Mumbai, India)– Project HERO Helpline for Emergency Response Operations – For patients seeking for immediate medical help

• Medical awareness in rural India– mMitra, info. service during pregnancy and childhood

emergency

Information bridge

Medical Emergency

MedicalResourc

es

http://www.armman.org/projecthero

http://www.armman.org/mmitra


Future Interoperability Challenge:360 degree health

Clinical CareInsurance, Financial Aspects

Genetic Tests… Profiles

Follow up,Lifestyle

Clinical Trials Social Media


• For each component in 360-degree health care, we have data, processes, knowledge and experience. Interoperability solutions need to encompass all these!– Possibly largest growth in data will be in

sensors (eg Body Area Networks, Biosensors) and social content. Extensive use of mobile phones.

Credit: ece.virginia.edu


Summary

• Semantic Web is an “interoperability technology”• Semantic Web provides the needed

interoperability, and can accommodate all necessary “points of view”

• Linked Data as a way of sharing data is highly promising

• Many examples of viable usage of Semantic Web technologies

• Words of warning about deployment• Significant research challenges remain as Health

presents the most complex domain


Representative References

1. A. Sheth, S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, and K. Gallagher, Active Semantic Electronic Medical Record, Intl Semantic Web Conference, 2006.

2. Satya Sahoo, Olivier Bodenreider, Kelly Zeng, and Amit Sheth, An Experiment in Integrating Large Biomedical Knowledge Resources with RDF: Application to Associating Genotype and Phenotype Information WWW2007 HCLS Workshop, May 2007.

3. Satya S. Sahoo, Kelly Zeng, Olivier Bodenreider, and Amit Sheth, From "Glycosyltransferase to Congenital Muscular Dystrophy: Integrating Knowledge from NCBI Entrez Gene and the Gene Ontology, Amsterdam: IOS, August 2007, PMID: 17911917, pp. 1260-4

4. Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner , Amit P. Sheth, An ontology-driven semantic mash-up of gene and biological pathway information: Application to the domain of nicotine dependence, Journal of Biomedical Informatics, 2008.

5. Cartic Ramakrishnan, Krzysztof J. Kochut, and Amit Sheth, "A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic Web Conference, 2006, pp. 583-596

6. Satya S. Sahoo, Christopher Thomas, Amit Sheth, William S. York, and Samir Tartir, "Knowledge Modeling and Its Application in Life Sciences: A Tale of Two Ontologies", 15th International World Wide Web Conference (WWW2006), Edinburgh, Scotland, May 23-26, 2006.

7. Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and Krishnaprasad Thirunarayan, 'Provenance Context Entity (PaCE): Scalable provenance tracking for scientific RDF data.’ SSDBM, Heidelberg, Germany 2010.

• Papers: http://knoesis.org/library• Demos at: http://knoesis.wright.edu/library/demos/

http://knoesis.wright.edu/library/resource.html?id=00004

http://www2007.org/workshops/paper_149.pdf

http://www2007.org/workshops/paper_149.pdf

http://www2007.org/workshop-W2.php









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semantic (web) technologies for translational research in life sciences

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