knowledge enabled information and services science semantic web for health care and biomedical...

Knowledge Enabled Information and Services Science

Semantic Web for Health Care and Biomedical Informatics

Keynote at

NSF Biomed Web Workshop, December 4-5, 2007

Amit P. [email protected]

Thanks Pablo Mendes, Satya Sahoo and Kno.e.sis team;Collaborators at Athens Heart Center (Dr. Agrawal), NLM (Olivier Bodenreider), CCRC, UGA (Will York), CCHMC (Bruce Aronow)


Outline

• Semantic Web – very brief intro

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


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 WebSemantic Web research aims at research aims atproviding this connection!providing this connection!

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


Evolution of the Web

Web of pages - text, manually created links - extensive navigation

2007

1997

Web of databases - dynamically generated pages - web query interfaces

Web of services - data = service = data, mashups - ubiquitous computing

Web of people - social networks, user-created content - GeneRIF, Connotea

Web as an oracle / assistant / partner - “ask to the Web” - using semantics to leverage text + data + services + people


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

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

• Reasoning/computation: semantics enabled search, integration, complex queries, analysis (paths, subgraph), pattern finding, mining, hypothesis validation, discovery, visualization

Semantic Web Enablers and Techniques


Maturing capabilites and ongoing research

• Text mining: Entity recognition, Relationship extraction

• Integrating text, experimetal data, curated and multimedia data

• Clinical and Scientific Workflows with semantic web services

• Hypothesis driven retrieval of scientific literature, Undiscovered public knowledge


Metadata and Ontology: Primary Semantic Web enablers

Shallow semantics

Deep semantics

Expr

essi

vene

ss,

Rea

soni

ng


Characteristics of Semantic Web

SelfDescribing

Machine &Human

Readable

Issued bya TrustedAuthority

Easy toUnderstand

ConvertibleCan be

Secured

The Semantic Web:XML, RDF & Ontology

Adapted from William Ruh (CISCO)


Open Biomedical Ontologies

Open Biomedical Ontologies, http://obo.sourceforge.net/

Many ontologies exist


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


Opportunity: exploiting clinical and biomedical data

text

Health Information

Services

Elsevier iConsult

Scientific Literature

PubMed300 Documents

Published Online each day

User-contributed Content (Informal)

GeneRifs

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.

binary


Scenario 1:

• Status: In use today

• Where: Athens Heart Center

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


Operational since January 2006


Goals:• Increase efficiency with decision support

• formulary, billing, reimbursement

• real time chart completion

• automated linking with billing

• Reduce Errors, Improve Patient Satisfaction & Reporting• drug interactions, allergy, insurance

• Improve Profitability

Technologies:• Ontologies, semantic annotations & rules

• Service Oriented Architecture

Thanks -- Dr. Agrawal, Dr. Wingeth, and others. ISWC2006 paper

Active Semantic Electronic Medical Records (ASEMR)


Demonstration


0

100

200

300

400

500

600

Month/Year

Ch

art

s

Same Day

Back Log

Chart Completion before the preliminary deployment

ASMER Efficiency

0100200300400500600700

Sept05

Nov 05 Jan 06 Mar 06

Month/Year

Ch

arts Same Day

Back Log

Chart Completion after the preliminary deployment


Scenario 2:

• Status: Demonstration

• Where: W3C Health Care and Life Sciences (HCLS) interest group

• What: Using semantic web to aggregate and query data about Alzheimer’s

• http://www.w3.org/2001/sw/hcls/


Scenario 2: Scientific Data Sets for Alzheimer’s


SPARQL Query spanning multiple sources


Scenario 3

• Status: Completed research• Where: NIH • What: Understanding the genetic basis of

nicotine dependence. 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).


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)


Deductive Reasoning Protein-Protein Interaction RULE: given that two genes interact with each other, given certain number of parameters being met, we can assert that the gene products also interact with each other

IF (x have_common_pathway y) AND (x rdf:type gene) AND (y rdf:type gene) AND (x has_product m) AND (y has_product n) AND (m rdf:type gene_product)AND (n rdf:type gene_product) THEN (m ? n)

gene_product

gene_producthas_product

have

_com

mon

_pat

hway

gene2

gene1has_product

database_identifier 2associated_with

associated_withdatabase_identifier 1

inte

ract

s_w

ith


Scenario 4

• Status: Completed research

• Where: NIH

• What: queries across integrated data sources– Enriching data with ontologies for integration,

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

relationships


Use data to test hypothesis

gene

GO

PubMed

Gene name

OMIM

Sequence

Interactions

Glycosyltransferase

Congenital muscular dystrophy

Link between glycosyltransferase activity and Link between glycosyltransferase activity and congenital muscular dystrophy?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”) }


Scenario 5

• Status: Research prototype and in progress• Workflow withSemantic Annotation of Experimental

Data already in use

• Where: UGA

• What:– Knowledge driven query formulation– Semantic Problem Solving Environment (PSE)

for Trypanosoma cruzi (Chagas Disease)


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


T.Cruzi PSE Query Interface

Figure 4: Semantic annotation of ms scientific data


N-GlycosylationN-Glycosylation ProcessProcess (NGPNGP)

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

Semantic Web Process to incorporate provenance


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

ProPreO: Ontology-mediated provenance

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”/>








</ms-ms_peak_list>

OntologicalConcepts

ProPreO: Ontology-mediated provenance

Semantically Annotated MS Data


Scenario 6

• When: Research in progress

• Where: Athens Heart Center and Cincinatti Children’s Hospital Medical Center

• What: scientific literature mining– Dealing with unstructured information– Extracting knowledge from text– Complex entity recognition– Relationship extraction


Heart Failure Clinical Pathway

causes

Disease

AngiotensionReceptor

Blocker (ARB)

Ontology: A Framework for Schema-Driven Relationship Discovery from Unstructured Text, Ramakrishnan, et. al., ISWC 2006, LNCS 4273, pp. 583-596


Contextual delivery of information


• Two technical challenges– Text mining– Workflow adaptation


Diabetes mellitus adversely affects the outcomes in patients with myocardial infarction (MI), due in part to the exacerbation of left ventricular (LV) remodeling. Although angiotensin II type 1 receptor blocker (ARB) has been demonstrated to be effective in the treatment of heart failure, information about the potential benefits of ARB on advanced LV failure associated with diabetes is lacking. To induce diabetes, male mice were injected intraperitoneally with streptozotocin (200 mg/kg). At 2 weeks, anterior MI was created by ligating the left coronary artery. These animals received treatment with olmesartan (0.1 mg/kg/day; n = 50) or vehicle (n = 51) for 4 weeks. Diabetes worsened the survival and exaggerated echocardiographic LV dilatation and dysfunction in MI. Treatment of diabetic MI mice with olmesartan significantly improved the survival rate (42% versus 27%, P < 0.05) without affecting blood glucose, arterial blood pressure, or infarct size. It also attenuated LV dysfunction in diabetic MI. Likewise, olmesartan attenuated myocyte hypertrophy, interstitial fibrosis, and the number of apoptotic cells in the noninfarcted LV from diabetic MI. Post-MI LV remodeling and failure in diabetes were ameliorated by ARB, providing further evidence that angiotensin II plays a pivotal role in the exacerbated heart failure after diabetic MI.

ARB causes heart failure

Extracting the Relationship

Angiotensin II type 1 receptor blocker attenuates exacerbated left ventricular remodeling and failure in diabetes-associated myocardial infarction.,Matsusaka H, et. al.


Problem – Extracting relationships between MeSH terms from PubMed

Biologically active substance

LipidDisease or Syndrome

affects

causes

affectscauses

complicates

Fish Oils Raynaud’s Disease???????

instance_of instance_of

UMLS Semantic Network

MeSH

PubMed9284 documents

4733 documents

5 documents


Background knowledge used

• UMLS – A high level schema of the biomedical domain– 136 classes and 49 relationships– Synonyms of all relationship – using variant lookup

(tools from NLM)– 49 relationship + their synonyms = ~350 mostly verbs

• MeSH – 22,000+ topics organized as a forest of 16 trees– Used to query PubMed

• PubMed – Over 16 million abstract– Abstracts annotated with one or more MeSH terms

T147—effect T147—induce T147—etiology T147—cause T147—effecting T147—induced


Method – Parse Sentences in PubMed

SS-Tagger (University of Tokyo)

SS-Parser (University of Tokyo)

(TOP (S (NP (NP (DT An) (JJ excessive) (ADJP (JJ endogenous) (CC or) (JJ exogenous) ) (NN stimulation) ) (PP (IN by) (NP (NN estrogen) ) ) ) (VP (VBZ induces) (NP (NP (JJ adenomatous) (NN hyperplasia) ) (PP (IN of) (NP (DT the) (NN endometrium) ) ) ) ) ) )

• Entities (MeSH terms) in sentences occur in modified forms• “adenomatous” modifies “hyperplasia”• “An excessive endogenous or exogenous stimulation” modifies “estrogen”

• Entities can also occur as composites of 2 or more other entities• “adenomatous hyperplasia” and “endometrium” occur as “adenomatous hyperplasia of the endometrium”


Method – Identify entities and Relationships in Parse Tree

TOP

NP

VP

S

NPVBZ

induces

NPPP

NPINof

DTthe

NNendometrium

JJadenomatous

NNhyperplasia

NP PP

INby

NNestrogenDT

the

JJexcessive ADJP NN

stimulation

JJendogenous

JJexogenous

CCor

MeSHIDD004967MeSHIDD006965 MeSHIDD004717

UMLS ID

T147

ModifiersModified entitiesComposite Entities


• What can we do with the extracted knowledge?

• Semantic browser demo


PubMed

Complex Query

SupportingDocument setsretrieved

Migraine

Stress

Patient

affects

isaMagnesium

Calcium Channel Blockers

inhibit

Keyword query: Migraine[MH] + Magnesium[MH]

Evaluating hypotheses


Workflow Adaptation: Why and How

• Volatile nature of execution environments– May have an impact on multiple activities/ tasks in the

workflow

• HF Pathway – New information about diseases, drugs becomes

available – Affects treatment plans, drug-drug interactions

• Need to incorporate the new knowledge into execution– capture the constraints and relationships between

different tasks activities


Workflow Adaptation Why?

New knowledge abouttreatment found duringthe execution of the pathway

New knowledge about drugs,drug drug interactions


Workflow Adaptation: How

• Decision theoretic approaches– Markov Decision Processes

• Given the state S of the workflow when an event E occurs– What is the optimal path to a goal state G– Greedy approaches rely on local optimization

• Need to choose actions based on optimality across the entire horizon, not just the current best action

– Model the horizon and use MDP to find the best path to a goal state


Conclusion

• semantic web technologies can help with:– Fusion of data: semi-structured, structured,

experimental, literature, multimedia– Analysis and mining of data, extraction,

annotation, capture provenance of data through annotation, workflows with SWS

– Querying of data at different levels of granularity, complex queries, knowledge-driven query interface

– Perform inference across data sets


Take home points

• Shift of paradigm: from browsing to querying

• Machine understanding: – extracting knowledge from text– Inference, software interoperation

• Semantic-enabled interfaces towards hypothesis validation


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, submitted, 2007.

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.

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

knowledge enabled information and services science semantic web for health care and biomedical...

Documents

services science semantic

web web of pages

knowledge enabled information

connotea web

web of databases

services science ontology

services science metadata

services science evolution