Literature-Based Knowledge Discovery using Natural Language Processing

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Literature-Based Knowledge Discovery using
Natural Language Processing
Dimitar Hristovski,1 PhD, Carol Friedman,2 PhD,
Thomas C Rindflesch,3 PhD, Borut Peterlin,4 MD
PhD 1Institute of Biomedical Informatics,
Medical Faculty, University of Ljubljana,
Slovenia 2Department of Biomedical Informatics,
Columbia University, New York3National Library
of Medicine, Bethesda, Maryland 4Division of
medical genetics, UMC, Slajmerjeva 3, Ljubljana,
Slovenia e-mail dimitar.hristovski_at_mf.uni-lj.si
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Part 1 Co-occurrence based LBD
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Motivation

• Overspecialization
• Information overload
• Large databases
• Need and opportunity for computer supported
  knowledge discovery

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Literature-based Discovery (LBD)

• A method for automatically generating hypotheses
  (discoveries) from literature
• Hypotheses have formConcept1 Relation
  Concept2
• ExampleFish oil Treats Raynauds disease

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Background

• Swansons LBD paradigm

New Relation?e.g. Treats
Concept X(Disease) e.g. Raynauds
Concepts Y(Pathologycal or Cell Function,
) e.g. Blood viscosity
Concepts Z(Drugs, ) e.g. Fish oil
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Biomedical Discovery Support System (BITOLA)

• Goal
• discover potentially new relations (knowledge)
  between biomedical concepts
• to be used as research idea generator and/or as
• an alternative way to search Medline
• System user (researcher or intermediary)
• interactively guides the discovery process
• evaluates the proposed relations

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Extending and Enhancing Literature Based
Discovery

• Goal
• Make literature based discovery more suitable for
  disease candidate gene discovery
• Decrease the number of candidate relations
• Method
• Integrate background knowledge
• Chromosomal location of diseases and genes
• Gene expression location
• Disease manifestation location

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System Overview
Knowledge Base
Concepts
Background Knowledge (Chromosomal Locations, )
Discovery Algorithm
Association Rules
User Interface
Knowledge Extraction
Databases (Medline, LocusLink, HUGO, OMIM, )
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Terminology Problems during Knowledge Extraction

• Gene names
• Gene symbols
• MeSH and genetic diseases

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Detected Gene Symbols by Frequency

• type666548
• II552584
• III201776
• component179643
• CT175973
• AT151337
• ATP147357
• IV123429
• CD499657
• p5389357
• MR88682
• SD85889
• GH84797
• LPS68982
• 5967272
• E264616

• 8263521
• AMP61862
• TNF59343
• RA58818
• CD857324
• O256847
• ACTH54933
• CO253171
• PKC51057
• EGF50483
• T349632
• MS46813
• A244896
• ER43212
• upstream41820
• PRL41599

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Gene Symbol Disambiguation

• Find MEDLINE docs in which we can expect to find
  gene symbols
• Example of false positive
• Ethics in a twist "Life Support", BBC1. BMJ 1999
  Aug 7319(7206)390
• breast basic conserved 1 (BBC1) gene, v.s. BBC1
  television station featuring new drama series
  Life Support

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Binary Association Rules

• X?Y (confidence, support)
• If X Then Y (confidence, support)
• Confidence of docs containing Y within the X
  docs
• Support number (or ) of docs containing both X
  and Y
• The relation between X and Y not known.
• Examples
• Multiple Sclerosis ? Optic Neuritis (2.02, 117)
• Multiple Sclerosis ? Interferon-beta (5.17, 300)

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Discovery Algorithm
Candidate Gene?
Concepts Y(Pathologycal or Cell Function, )
Concept X(Disease)
Concepts Z(Genes)
Chromosomal Region
Chromosomal Location
Match
Manifestation Location
Expression Location
Match
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Ranking Concepts Z
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Problem Size

• Full Medline analyzed (cca 15,000,000 recs)
• 87,000,000 association rules between 180,000
  biomedical concepts

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Bilateral Perisylvian Polymicrogiria - BPP (OMIM
300388)

• Polymicrogyria of the cerebral cortex is a
  developmental abnormality characterized by
  excessive surface convolution
• Clinical characteristics
• Mental retardation
• Epilepsy
• Pseudobulbar palsy (paralysis of the face,
  throat, tongue and the chewing process)
• X linked dominant inheritance

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237 genes in Xq28
relation between semantic types Cell Movement and
Gene or gene products
18 gene candidates
Sublocalisation in the Xq28
15 gene candidates
Tissue specific expression
2 gene candidates L1CAM and FLNA
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User Interface cgi-bin version
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Automatically search for supporting Medline
Citations
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Part 1 Summary and Conclusions

• Discovery support system (BITOLA) presented
• The system can be used as
• Research idea generator, or
• Alternative method of searching Medline
• Genetic knowledge about the chromosomal locations
  of diseases and genes included to make BITOLA
  more suitable for disease candidate gene discovery

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System Availability

• URL www.mf.uni-lj.si/bitola/

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Part 2 Exploring Semantic Relations for LBD
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Current LBD Systems

• Co-occurrence based
• Concepts
• Title/Abstract Words/Phrases
• MeSH
• UMLS
• Genes ...
• UMLS Semantic types used for filtering
• Semantic relations between concepts NOT used

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Drawbacks of Current LBD

• Not all co-occurrences represent a relation
• Users have to read many Medline citations when
  reviewing candidate relations
• Many spurious (false-positive) relations and
  hypotheses produced
• No explanation of proposed hypotheses

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Enhancing the LBD paradigm

• Use semantic relations obtained from
• two NLP systems (BioMedLee and SemRep) to
  augment
• co-occurrence based LBD system (BITOLA)

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Methods
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Discovery Patterns

• Discovery pattern Set of conditions to be
  satisfied for the generation of new hypotheses
• Conditions are combinations of semantic relations
  between concepts
• Maybe_Treats pattern in this research has two
  forms
• Maybe_Treats1
• Maybe_Treats2

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Maybe_Treats Discovery Pattern
Maybe_Treats1
Substance Y1(or Body meas., Body funct.)
Drug Z1 (or substance)
Opposite_Change1
Change1
Disease X
Disease X2
Substance Y2(or Body meas., Body funct.)
Same Change2
Change2
Treats
Drug Z2(or substance)
Maybe_Treats2
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Maybe_Treats1 and Maybe_Treats2

• GoalPropose potentially new treatments
• Can work in concert
• Propose different treatments (complementary)
• Propose same treatments using different discovery
  reasoning (reinforcing)

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Multiple Usages of Maybe_Treats

• Given Disease X as input
• find new treatments Z
• Given Drug Z as input
• find diseases X that can be treated
• Given Disease X and Drug Z as input
• test whether Z can be used to treat X

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Semantic Relations Used

• Associated_with_change and Treats used to extract
  known facts from the literature
• Then Maybe_Treats1 and Maybe_Treats2 predict new
  treatments based on the known extracted facts

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Associated_with_change

• One concept associated with a change in another
  concept, for example
• Associated_with(Raynauds, Blood viscosity,
  increase)
• Local increase of blood viscosity during
  cold-induced Raynaud's phenomenon.
• Increased viscosity might be a causal factor in
  secondary forms of Raynaud's disease,
• BioMedLee (Friedman et al) used to extract
  Associated_with_change

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Treats

• Used to extract drugs known to treat a disease
• Major purpose in our approach
• Eliminate drugs already known to be used to treat
  a disease
• Find existing treatments for similar diseases
• TREATS(Amantadine,Huntington)
• treatment of Huntingtons disease with
  amantadine
• Treats extracted by SemRep (Rindflesch et al)

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Results
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Huntington Disease

• Inherited neurodegenerative disorder
• All 5511 Huntington citations (Jan.2006)
  processed with BioMedLee and SemRep
• 35 interesting concepts assoc.with change
  selected and corresponding citations (250.000)
  processed

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Insulin for Huntington Disease

• Assoc_with(Huntington,Insulin,decrease)
• Huntington's disease transgenic mice develop an
  age-dependent reduction of insulin mRNA
  expression and diminished expression of key
  regulators of insulin gene transcription,
• Insulin also decreased in diabetes mellitus
• Therapies used to regulate insulin in diabetes
  might be used for Huntington

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Capsaicin for Huntington

• Assoc_with(Huntington,Substance P,decrease)
• In Huntington's disease brains decreased
  Substance P staining was found in
• Assoc_with(Capsaicin,Substance P,increase)
• Capsaicin also attenuated the increase in
  Substance P content in sciatic nerve,
• Capsaicin maybe treats Huntington because
  Substance P is decreased in Huntington and
  Capsaicin increases Substance P.

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Huntington Results - Summary
Maybe_Treats1
Substance P(Substance Y1)
Capsaicin(Drug Z1)
Increase
Decrease
Huntington(Disease X)
Diabetes M(Disease X2)
Insulin(Substance Y2)
Decrease
Decrease
Treats
Insulin regulation ther. (Z2)
Maybe_Treats2
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Example Parkinson disease as starting concept.
Bellow shown some related concepts changed in
association to Parkinson
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Potential Treatments for Parkinson (e.g.
gabapentine)
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Showing Supporting Sentenceswith highlighted
concepts and relations
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Gabapentine for Parkinson

• Assoc_with(Parkinson,gamma-aminobutyric
  acid(GABA),decrease)
• studies indicate that patients with Parkinson's
  disease have decreased basal ganglia
  gamma-aminobutyric acid function
• Assoc_with(GABA,Gabapentine,increase)
• Gabapentin, probably through the activation of
  glutamic acid decarboxylase, leads to the
  increase in synaptic GABA.
• Explanation Gabapentine maybe treats Parkinson
  because GABA is decreased in Parkinson and
  Gabapentine increases GABA.

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Part 2 Conclusions

• A new method to improve LBD presented
• Based on discovery patterns and semantic
  relations extracted by BioMedLee and SemRep,
  coupled with BITOLA LBD
• Easier for the user to evaluate smaller number of
  hypotheses
• Two potentially new therapeutic approaches for
  Huntington proposed and one for Parkinson
• RaynaudsFish oil discovery replicated

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The future of Literature-based Discovery

• Development of specific discovery patterns based
  on semantic relations and further integrated with
  co-occurrence-based LBD

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Link, References and some propaganda

• http//www.mf.uni-lj.si/bitola
• Hristovski D, Peterlin B, Mitchell JA and
  Humphrey SM. Using literature-based discovery to
  identify disease candidate genes. Int. J. Med.
  Inform. 2005. Vol. 74(24), pp. 289298. ?
  Selected for Yearbook of Medical Informatics 2006
• Hristovski D, Friedman C, Rindflesch TC, Peterlin
  B. Exploiting semantic relations for
  literature-based discovery. In Proc AMIA 2006
  Symp 2006. p. 349-53.
• Ahlers C, Hristovski D, Kilicoglu H, Rindflesch
  TC. Using the Literature-Based Discovery Paradigm
  to Investigate Drug Mechanisms. In Proc AMIA 2007
  Symp 2007. p. 6-10. ? Distinguished Paper Award
  AMIA2007
• Hristovski D, Friedman C, Rindflesch TC, Peterlin
  B. Literature-Based Knowledge Discovery using
  Natural Language Processing. ? To appear as a
  chapter in the first LBD book in 2008