Literature Based Discovery

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Literature Based Discovery

• Dimitar Hristovski dimitar.hristovski_at_mf.uni-lj.
  si
• Institute of Biomedical Informatics, Faculty of
  Medicine, University of Ljubljana, Ljubljana,
  Slovenia

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Let me introduce myself Research and Development

• BS Biomedicina Slovenica database
• Research Evaluation Decision Support System
• Medical Information Systems
• Surgical clinics
• Genetic laboratory
• Biochemical laboratory
• Web User Behaviour Analysis
• Data warehousing and OLAP

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Motivation

• Overspecialization
• Information overload
• Large databases
• For many diseases the chromosomal region known,
  but not the exact gene

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Background

• Literature-based discovery (Swanson)

New Relation?
Concept X(Disease)
Concepts Y(Pathologycal or Cell Function, )
Concepts Z(Genes)
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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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Usage Scenarios

• For a disease with known chromosomal location,
  find a candidate gene
• For a gene, find a disease that might be
  influenced
• For a disease and gene found to be related by
  linkage study, find the mechanism of the relation
  (intermediate concepts should help)

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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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Databases

• Medline source of known relationships between
  biomedical concepts
• Set of concepts
• MeSH (Medical Subject Headings) Controlled
  dictionary and thesaurus used for indexing and
  searching the Medline database
• HUGO official gene symbols, names and aliases
• LocusLink gene symbols, aliases and
  chr.locations
• OMIM genetic diseases
• UMLS (Unified Medical Language System)
• Entrez used to search PubMed, GenBank, ...
• UniGene gene expression

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Knowledge Extraction

• Build master set of concepts (MeSH terms and gene
  symbols)
• Extract occurrence of concepts from each Medline
  record (MeSH terms from MH field, gene symbols
  from Title and Abstract)
• Association rule mining (concept co-occurrence)
• Chromosomal location extraction (from LocusLink
  and HUGO)
• Load into knowledge base

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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
• JD indexing (Susanne Humphrey) as possible
  solution
• Identifies the semantic context of docs
• If semantic context not genetic, then gene symbol
  probably false positive
• 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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JD Indexing

• JDs are 127 Journal Descriptors (e.g., JDs for
  journal Hum Mol Genet Cytogenetics Genetics,
  Medical)
• Training set docs (435,000) inherit JDs from
  journals
• Training set provides co-occurrence data between
  inherited JDs and
• indexing terms assigned to docs directly
• words in docs
• Docs having indexing terms/words occurring often
  with genetics JDs in tr. set assumed to have
  genetics context
• Extended to indexing by 134 UMLS semantic types
  (e.g. Gene or Genome, Gene Function,)

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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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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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Discovery Algorithm

• Let X be starting concept of interest.
• Find all Y for which X ? Y.
• Find all Z for which Y ? Z.
• Eliminate those Z for which X-gtZ already exists.
• Eliminate those Z that do not match the
  chromosomal region of X
• Eliminate those Z that do not match the
  expression location of X
• Remaining Z are candidates for new relation
  between X and Z.
• In general
• X ? Y1 ? ? Yn ? Z, but not X ? Z

• Example
• X disease
• Y (pato)physiology of X
• Z (de)regulators of Y (drugs, proteins, genes)
• New relation example Z is candidate gene for
  disease X

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Ranking Concepts Z
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Results Concepts in Medline

• Full Medline (end 2001) analyzed (11,226,520
  recs)
• Looking for 19,781 MeSH terms and 22,252 human
  genes (14,659 from HUGO and 7,593 from
  LocusLink). 24,613 alias gene symbols added
• Gene symbols found in 2,689,958 Medline recs.
  Most frequent ambiguous symbols (CT, MR, CO2,)
  or format errors

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Results Co-occurring Concepts in Medline

• 29,851,448 distinct pairs of co-occurring
  concepts
• In 7,106,099 at least one gene symbol appeared
• In 679,159 pairs both concepts are gene symbols
• Total co-occurrence frequency 798,366,684
• 59,702,986 association rules calculated and
  stored

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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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BPP - pathogenesis

• It is considered a disorder of neuronal migration
  (unlayered type) or a consequence of intrauterine
  ischemia (layered type)

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Finding Candidate Genes for Polymicrogyria,
bilateral perisylvan
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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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Cleft Palate Predicting Candidate Genes
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Summary and Conclusions

• We extend and enhance an existing discovery
  support system (BITOLA)
• 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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Further Work

• Increase the number of concepts
• Gene symbol disambiguation
• Semantic relations extraction
• System evaluation
• Improve the Web version of the system

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System Availability

• URL www.mf.uni-lj.si/bitola/

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Related work SemGenTom Rindflesch et al

• Extract semantic predications on genetic basis of
  disease
• Deletions of INK4 occur in malignant tumors
• INK4ASSOCIATED_WITHMalignant Tumors
• Evaluation and visualization of SemGen output

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Semantic Structures
ltgenetic phenomenongt
ETIOLOGY_OF
ltdisordergt
CAUSE
PREDISPOSE
ASSOCIATED_WITH
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Statistical Evaluation

• Assoc. rule base divided into 2 segments older
  (1990-1995) and newer (1996-1999)
• The system predicts new relations based on the
  older segment
• Predictions compared with actual new relations in
  the newer segment

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Summary Statistical Evaluation Results
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Statistical Evaluation Results

• With no assoc. rules constraints
• predicts almost all new relations, but too many
  candidate relations
• With constraints
• predicts new relations 6.9 times better than
  random predictions
• tighter the constraints, better (correct / all
  predictions) ratio (6.5)