literature-based knowledge discovery using natural language processing
DESCRIPTION
Literature-Based Knowledge Discovery using Natural Language Processing. Dimitar Hristovski, 1 PhD, Carol Friedman, 2 PhD, Thomas C Rindflesch, 3 PhD, B orut Peterlin, 4 MD PhD 1 Institute of Biomedical Informatics, Medical Faculty, University of Ljubljana, Slovenia - PowerPoint PPT PresentationTRANSCRIPT
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Literature-Based Knowledge Discovery using
Natural Language ProcessingDimitar 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: [email protected]
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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 form:Concept1 –Relation– Concept2
• Example:Fish oil –Treats– Raynaud’s disease
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Background • Swanson’s LBD paradigm:
Concept X(Disease)e.g. Raynaud’s
Concepts Y(Pathologycal or Cell Function, …)e.g. Blood viscosity
Concepts Z(Drugs, …)e.g. Fish oil
New Relation?e.g. Treats
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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
Association Rules
Background Knowledge (Chromosomal Locations, …)
Discovery Algorithm
User Interface
Databases (Medline, LocusLink, HUGO, OMIM, …)
Knowledge Extraction
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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
• type|666548• II|552584• III|201776• component|179643• CT|175973• AT|151337• ATP|147357• IV|123429• CD4|99657• p53|89357• MR|88682• SD|85889• GH|84797• LPS|68982• 59|67272• E2|64616
• 82|63521• AMP|61862• TNF|59343• RA|58818• CD8|57324• O2|56847• ACTH|54933• CO2|53171• PKC|51057• EGF|50483• T3|49632• MS|46813• A2|44896• ER|43212• upstream|41820• PRL|41599
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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 7;319(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• XY (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
Concept X(Disease)
Concepts Y(Pathologycal or Cell Function, …)
Concepts Z(Genes)
Chromosomal Region
Chromosomal Location
Candidate Gene?
Match
Manifestation Location
Expression Location
Match
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Ranking Concepts Z
X
Y1
Y2
Y3
Yi
Yj
…
…
Z1
Z2
Z3
Zk
Zn
s1
( ) ( * )i i k
m
k XY Y Zi
Rank Z S S
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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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18 gene candidates
15 gene candidates
Tissue specific expression
2 gene candidates: L1CAM and FLNA
relation between semantic types Cell Movement and Gene or gene products
Sublocalisation in the Xq28
237 genes in Xq28
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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
Disease X
Maybe_Treats2
Change1
Change2
Treats
Substance Y1(or Body meas.,
Body funct.)
Substance Y2(or Body meas.,
Body funct.)
Drug Z1 (or substance)
Disease X2
Drug Z2(or substance)
Opposite_Change1
Same Change2
Maybe_Treats1
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Maybe_Treats1 and Maybe_Treats2
• Goal:Propose 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(Raynaud’s, 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 Huntington’s 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
Huntington(Disease X)
Maybe_Treats2
Decrease
Decrease
Treats
Substance P(Substance Y1)
Insulin(Substance Y2)
Capsaicin(Drug Z1)
Diabetes M(Disease X2)
Insulin regulation ther.
(Z2)
Increase
Decrease
Maybe_Treats1
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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 Sentences
with 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
• Raynaud’s—Fish 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(2–4), pp. 289–298. 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