Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for Literature-based Discovery

A CONTEXT-DRIVEN SUBGRAPH MODEL FOR
LITERATURE-BASED DISCOVERY
PH.D. DISSERTATION DEFENSE
DELROY CAMERON
AUGUST 18, 2014
PH.D. COMMITTEE
AMIT P. SHETH (ADVISOR)
KRISHNAPRASAD THIRUNARAYAN
MICHAEL RAYMER
RAMAKANTH KAVULURU (UKY)
THOMAS C. RINDFLESCH (NIH)
VARUN BHAGWAN (YAHOO! LABS)All truths are easy to understand once they are discovered;
the point is to discover them. (Galileo Galilei, 1564–1642)

2
Historical Perspectives
Walter Sutton
(1877 – 1916)
Theodor Boveri
(1862 – 1915)
Gregor Johann Mendel
(1822 – 1884)
Mendelian Laws of Inheritance
(1866)
Boveri-Sutton Chromosome Theory
(1903)

3
Science of Making Discoveries
Discovery
Information Processing
System
What is promising?

4
Thesis Statement
An information processing system that leverages rich representations
of textual content from scientific literature based on implicit and explicit
context can provide effective means for literature-based discovery.

5
Motivation
Rofecoxib Osteoarthritis1999 TREAT
Merck & Co.
Increased risk of
Heart Attack
2002
2004
$254.3 million
Settlement
2005
Vioxx
Withdrawn
$4.85 billion
Settlement
Confirmed by
Clinical Trial
2007 2011
$950 million
Settlement
2013
$23 million
Settlement

6
Motivation
Literature-Based Discovery (LBD)

7
Literature-Based Discovery (LBD)
ABC Model
AnC Model
Context-Driven Subgraph Model
A CB
A CB1 B2 BiSource: Wikipedia - http://en.wikipedia.org/wiki/Don_R._Swanson
Keyword-based
Concept-based
Relations-based
2006 20111986 1996
ARROWSMITH v1
Term Frequency
1999
IRIDESCENT
Term Co-occurrence
2001
DAD
MetaMAP
UMLS
2003
Litlinker
MeSH, UMLS, Rules
Level of Support
Contribution #1
Context-Driven
Subgraph Model for LBD
SemBT
Semantic Predications
Level of Support
Discovery Browsing
Degree Centrality
Cooperative Reciprocity
Manual
2013
Manjal
UMLS, MeSH
Topic Profiles, TF-IDF
2004
Rajolink
MeSH, Rarity
BioSbKDS
UMLS Relations
MeSH
2005
BITOLA
UMLS, MeSH
Assoc. Rules,
Confidence
Graph-based
ACS (2004)
MeSH,
Hebbian Learning
A CB
CAUSESINHIBITS
A C
PRODUCES
INHIBITS
Discovery Patterns
Hybrid
ARROWSMITH v2
8 Features (2007)
Semantic MEDLINE
Summarization
Discovery Browsing
Epiphanet
Predications-based
Semantic Indexing
CoPub
Keywords, Mutual
Information
2010
Literature-based discovery refers to the use of papers and other academic publications
(the “literature”) to find new relationships between existing knowledge (the “discovery”).
Definition courtesy of Wikipedia: http://en.wikipedia.org/wiki/Literature-based_discovery

8
Application: Raynaud Syndrome – Fish Oil
ISA
Prostaglandin I3
CONVERTS_TO
Dietary
Fish Oils
Platelet
Aggregation
DISRUPTS
ISA
DISRUPTS
DISRUPTS
Epoprostenol
DISRUPTS
ISA
STIMULATES
Prostaglandin
CONVERTS_TO
Raynaud
Syndrome
TREATS
CAUSES
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition
of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13). 46(2): 238–251, 2013.
Dietary
Fish Oils
Platelet
Aggregation
Raynaud
Syndrome
DISRUPTS CAUSES
Dietary
Fish Oils
Platelet
Aggregation
Raynaud
Syndrome
Keyword/
Concept
based
Relations
based
Subgraph
based
Inferred predicates

9
Comparison
Scenario Intermediate Cameron [19]
Srinivasan
[88, 89]
Weeber
[101, 102]
Gordon
[36,37,38]
Hristovski
[40]
Raynaud
Syndrome –
Dietary Fish
Oils
Blood
Viscosity
× × × × ×
Platelet
Aggregation
× × × × ×
Vascular
Reactivity
× × × ×
Ramakrishnan
[72]*
?
?
?
Table 1: Comparison of intermediates rediscovered for Raynaud Syndrome – Dietary Fish Oil

DISRUPTS
ISA
ISA
Dietary
Fish Oils
Platelet
Aggregation
DISRUPTS
Raynaud
Syndrome
CAUSES
Prostaglandins
CONVERTS_TO
Prostacyclin
(PGI2)
DISRUPTS
Prostaglandin I3
(PGI3) TREATSSTIMULATES
Raynaud
Syndrome
Dietary
Fish Oils
Fatty Acid
Essential
Fatty Acid
Triglyceride
Lipid
ISA
DISRUPTS CAUSES
ISA
INHIBIT
AFFECTS
ISA
INHIBITS
Blood
Viscosity
Cellular
Activity
Blood
Physiology
Problem
How to automate this?
Tissue
Function
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis
using
DISRUPTS
ISA
Dietary
Fish Oils
Prostaglandin I3
(PGI3)
Prostacyclin
(PGI2)
Raynaud
Syndrome
CAUSESVasoconstrictionINHIBIT
CONVERTS_TO
AFFECTS DISRUPTS
TREATS

Literature-
Based
Discovery
Context-
Driven
Subgraph
Model
Foundations
Automatic
Subgraph
Creation
Experimental
Results
Dissertation
Contributions
Knowledge
Exploration
Limitations
& Future
Work

13
. . .
Subgraph Model
Predications
Graph (G)
Candidate
Graph (RG)
Subgraphs (SG)
No two contexts are the same
R(s,t)(c1) R(s,t)(c2) R(s,t)(ck)
R(s,t)
. . .
. . .
What is context?

Literature-
Based
Discovery
Context-
Driven
Subgraph
Model
Foundations
Automatic
Subgraph
Creation
Experimental
Results
Dissertation
Contributions
Knowledge
Exploration
Limitations
&
Future
Work

15
• Path Relatedness
• Semantic Predication Context
Context Distribution Assumption: The context of a semantic predication
can be expressed as the distribution of all MeSH descriptors associated
with all articles that contain it.
Semantic Underpinnings
Relational
Semantic
Summary
Textual
Semantic
Summary
Concept-Level
Semantic
Summary
Interchangeability Assumption: The concept-level and relational semantic
summary of a MEDLINE article are interchangeable.

16
Linguistic Underpinnings
Linguistic items with similar distributions have similar meanings
“You shall know a word
by the company it keeps”
– J. R. Firth 1957
Semantic Predications with shared contexts in their distributions are related
Distributional Semantics
Context-sensitive nature of meaning

18
MeSH Hierarchy
MeSH Hierarchy
Automatic Subgraph Creation
m1 m2
m7 m8
m1 m7 m2 m8
m
1
m5 m9 m
8
Semantic Relatedness
of MeSH Context Vectorsm9m1
m5 m8
Contribution #2
Context of a path
as a vector of
MeSH Descriptors
pi
pj

19
Path Relatedness
3 32
5 42
2
53 6
Objective #1: Maximize weights of In-Context Descriptors
Objective #2: Minimize weights of Out-Of-Context Descriptors
C(pi)
C(pj)
1 3 1 2
2
3 00 00 02 0 0 03 22
5 42 53 61 3 1 20 00
p – path
t – semantic predication
m1 m2 m3 m4 m5
m1 m2 m6 m7 m8 m9 m10 m11 m12 m13
m1 m2 m6 m7 m8 m9 m10 m11 m12 m13m3 m4 m5
C(pi)
C(pj)

20
Path Relatedness: Shared Context
1 00 00 01 0 0 01 11
1 11 11 11 1 1 10 00
Platelet
aggregation
Platelet
activation
Epoprostenol
Platelet
adhesiveness
Prostaglandinsm3 m4 m5 m9 m10 m11 m12 m13
G-Tree
platelet
aggregation
hemostasis
Blood
physiological
process
Blood
physiological
phenomena
Circulatory and respiratory
physiological phenomena
platelet
adhesiveness
platelet
activation Epoprostenol
D-Tree
Prostaglandins
I
Arachidonic
Acids
Fatty Acids,
Unsaturated
Fatty Acids
Lipids
Prostaglandins
Eicosanoids
Contribution #3
Structured Background Knowledge
for computing shared context of paths
C(pi)
C(pj)

21
Path Relatedness Score
*Dictionary of Distances, Elena Deza, Michel-Marie Deza, Elsevier, 2006

22
Hierarchical Agglomerative Clustering
A C A CA CA C A CA CA C A C
Iteration 1
Iteration n
. . .
Bucket PopulationBucket Merging
...
A C
A C
A C
A C
Path Relatedness Threshold
1. Bucket Population
2. Bucket Merging
3. Subgraph Ranking

23
Summary of Metrics
– Model: MeSH Context Vectors
– Metrics: Semantics-enhanced shared context, Log Reduction
– Threshold: ??
• MeSH Semantic Similarity
– Model: MeSH Hierarchy
– Metrics: Dice Similarity
– Threshold: Manually

24
Automatic Threshold Selection
RS-DFO Experiment
Manual Threshold = 3.0
Gaussian Distribution
NumberofPathPairs

25
Gaussian Function
ExpectedValue

26
• Gaussian Distribution
Diagram courtesy of Wikipedia*
Points of Inflection

27
Threshold Comparisons
Scenario
Max
2 Std Dev. Manual 3 Std Dev.
RS-DFO 2.68 3.0 3.04 3.38
Testosterone-Sleep 3.35 3.5 3.8262 6.22
DEHP-Sepsis 3.94 4.0 4.53 4.84
Table 2: Path Relatedness Threshold Comparisons

28
Bucket Merging
Ba
Bb
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to information retrieval. Cambridge University Press 2008,
ISBN 978-0-521-86571-5, pp. I-XXI, 1-482
Straggly Clusters Compact Clusters
Broad Clusters

29
Subgraph Ranking
Intra-Cluster Rank

30
Singleton Ranking
Association Rarity

31
Summary of Metrics
– Model: MeSH Context Vectors
– Metrics: Semantics-enhanced shared context, Log Reduction
– Manual Threshold for Semantic Similarity, Dice Similarity
– Threshold: 2nd Standard Deviation from Mean of Gaussian
• Bucket Relatedness
– Model: Set of Paths
– Metric: Inter-Cluster Similarity
– Threshold: 2nd Standard Deviation from Mean of Gaussian
• Subgraph Ranking
– Metrics: Intra-Cluster Similarity, Singleton Rank (Association Rarity)

32
Algorithm
Time Complexity: Θ(N 2logN )

34
Raynaud Syndrome – Dietary Fish Oil
Inferred predicates
Path Relatedness Threshold = 3σ

Scenario 1: Raynaud Syndrome – Dietary Fish Oil
Details Intermediate Association Status
Cut-off date:
Nov. 1985
By. D. R.
Swanson
(Article)
Blood Viscosity
Dietary Fish Oils INHIBITS Blood
Viscosity
Blood Viscosity CAUSES Raynaud
Syndrome
ZR-15
Platelet Aggregation
Dietary Fish Oils INHIBITS Platelet
Aggregation
Platelet Aggregation CAUSES Raynaud
Syndrome
S1
Vasoconstriction
Dietary Fish Oils INHIBITS
Vasoconstriction
Vasoconstriction CAUSES Raynaud
Syndrome
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found
Results available online: http://wiki.knoesis.org/index.php/Obvio#Automatic_Subgraph_Creation
Obvio Web Application: http://knoesis-hpco.cs.wright.edu/obvio/

Scenario 2: Magnesium – Migraine
Cut-off date:
Apr. 1987
By. D. R.
Swanson
(Article)
Calcium Channel Blockers
Magnesium ISA Calcium Channel
Blocker
Calcium Channel Blockers TREATS
Migraine
S22
Epilepsy Magnesium AFFECTS Epilepsy Epilepsy CO_EXISTS_WITH Migraine S9
Hypoxia Magnesium INHIBITS Hypoxia Hypoxia ASSOCIATED_WITH Migraine
Inflammation Magnesium INHIBITS Inflammation Inflammation CAUSES Migraine ZR-3
Platelet Activity
Magnesium INHIBITS Platelet
Aggregation
Platelet Aggregation CAUSES Migraine S1
Prostaglandins
Magnesium STIMULATES
Prostaglandins
Prostaglandins DISRUPTS Migraine S4
Stress/Type A Personality STRESS INHIBITS Magnesium Stress ASSOICATED_WITH Migraine
Serotonin Magnesium INHIBITS Serotonin Serotonin CAUSES Migraine S1
Cortical Depression
Magnesium INHIBITS Spreading
Cortical Depression
Spreading Cortical Depression CAUSES
Migraine
Substance P Magnesium INHIBITS Substance P Substance P CAUSES Migraine
Vascular Mechanisms
Magnesium INHIBITS
Vasoconstriction
Vasoconstriction CAUSES Migraine S9
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 3: Somatomedin C – Arginine
Cut-off date:
Apr. 1989
By. D. R.
Swanson
(Article)
Growth Hormone
Arginine STIMULATES Growth
Hormone
Growth Hormone STIMULATES
Somatomedins (IGF1)
S5
Body Weight (body mass)
Somatomedins (IGF1) STIMULATES
Growth
Arginine STIMULATES Growth S7
Malnutrition Somatomedins TREATS Malnutrition Arginine TREATS Malnutrition S7
Wound Healing (NK
activity)
Somatomedins STIMULATES Wound
Healing
Arginine STIMULATES Wound Healing
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 4: Indomethacin – Alzheimer’s Disease
Cut-off date:
Jul. 1995
By.
Swanson/Smal
heiser
(Article)
Acetylcholine Indomethacin INHIBITS Acetylcholine Acetylcholine CAUSES Alzheimers S4
Lipid Peroxidation
Indomethacin INHIBITS Lipid
Peroxidation
Lipid Peroxidation CAUSES Alzheimers S2
M2-Muscarinic
Indomethacin INHIBITS M2-
Muscarinic
M2-Muscarinic CAUSES Alzheimers
Membrane Fluidity
Indomethacin INHIBITS Membrane
Fluidity
Membrane Fluidity CAUSES Alzheimers
Lymphocytes
Indomethacin STIMULATES Natural
Killer T-Cell Activity
T-Cell Activity INHIBITS Alzheimers S14
Thyrotropin
Indomethacin STIMULATES
Thyrotropin
Thyrotropin AFFECTS Alzheimers ZR-20
T-lymphocytes (T-Cells)
Indomethacin STIMULATES T-
lymphocytes
T-lymphocyte Activity INHIBITS
Alzheimers
S3
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 5: Estrogen – Alzheimer’s Disease
Cut-off date:
Jul. 1995
By.
Swanson/Smal
heiser
(Article)
Antioxidant Activity Estrogen INHIBITS Antioxidant Activity Antioxidant Activity CAUSES Alzheimers S4
Aliproprotein E (ApoE) Estrogen INHIBITS ApoE ApoE CAUSES Alzheimers S3
Calbindin D28k
Estrogen REGULATES Caldindin
D28k
Calbindin D28k AFFECTS Alzheimers S4
Cathepsin D Estrogen STIMULATES Cathepsin D Cathepsin D PREVENTS Alzheimers
Cytochrome C Oxidase
Subunit III
Estrogen STIMULATES Cytochrome
C Oxidase Subunit III
Cytochrome C Oxidase Subunit III
AFFECTS Alzheimers
Glutamate Estrogen STIMULATES Glutamate Glutamate AFFECTS Alzheimers
Receptor Polymorphism
Estrogen EXHIBITS Receptor
Polymorphism
Receptor Polymorphism AFFECTS
Alzheimers
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 6: Calcium Independent PLA2 – Schizophrenia
Cut-off date:
1997
By.
Swanson/Smal
heiser
(Article)
Oxidative Stress
Oxidative Stress INHIBITS Calcium-
Independent PLA2
Oxidative Stress CAUSES Schizophrenia ZR-2
Selenium
Selenium INHIBITS Calcium-
Independent PLA2
Selenium PREVENTS Schizophrenia ZR-2
Vitamin E
Vitamin E INHIBITS Calcium-
Independent PLA2
Vitamin E PREVENTS Schizophrenia ZR-2
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 7: Chlorpromazine – Cardiac Hypertrophy
Cut-off date:
01/01/2002
By. J. D. Wren
(Article)
Calcineurin Chlorpromazine INHIBITS Calcineurin
Calcineurin CAUSES Cardiac
Hypertrophy
S5
Isoproterenol
Chlorpromazine INHIBITS
Isoproterenol
Isoproterenol CAUSES Cardiamegaly S12
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 8: Testosterone – Sleep
Cut-off date:
01/01/2012
By.
Miller/Rindflesc
h
(Article)
Cortisol/Hydrocortisone Testosterone INHIBITS Cortisol Cortisol DISRUPTS Sleep S7
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

Scenario 9: Diethylhexyl Phthalate (DEHP) – Sepsis
Cut-off date:
01/01/2013
By.
Cairelli/Rindfle
sch
(Article)
PParGamma DEHP STIMULATES PParGamma PParGamma INHIBITS Sepsis
Legend
ZR-zero rarity
singleton
S-Subgraph
Not Found

44
Statistical Evaluation
Association Rarity Interestingness

45
Statistical Evaluation
Experiment
# Unique
Associations
Total
MEDLINE
Frequency
Rarity
r(E)
Interestingness
I(E)
Raynaud-Fish Oil 10 0 0.00 1.00
Magnesium-Migraine 48 27 0.56 0.64
SomaC-Arginine 18 306 17.00 0.06
Indomethacin-
Alzheimers
21 9 0.43 0.70
Estrogen-Alzheimers 42 36 0.86 0.54
PLA2-Schizophrenia 10 0 0.00 1.00
CPZ-Cardiac
Hypertrophy
21 2 0.10 0.91
Testosterone-Sleep 61 654 10.72 0.09
Average 29 129 3.71 0.62
Table 3: Rarity and Interestingness score of the subgraphs in the rediscoveries

47
Predications-based Knowledge Exploration
Corpus
Predications Graph
Definitional Knowledge (UMLS + MeSH)
Provenance
Knowledge Abstraction
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature
International Bioinformatics and Biomedical Conference (BIBM11). 512–519 , 2011.
Contribution #4
Combining Assertional and
Definitional Knowledge
for Knowledge Exploration

48
Levels of Contexts
A CB
Predication
Context
A CB1 B2 Bi
Path
Context
A CB1 B2 B3
A CB1 B2
Shared
Context
A C
PRODUCES
INHIBITS
Subgraph
Context
…
…
…
…
…
…
A C
A C
A C
…
Dimensions

50
Dissertation Contributions
1. Context-Driven Subgraph Model
– Knowledge Rediscovery & Decomposition
2. Predication/Path Context
– Vector of MeSH Descriptors
3. Shared Context
– Background Knowledge (MeSH Hierarchy)
4. Semantic Predications-based Text Exploration
– Obvio Web Application

51
Innovation
System/Technique
Technique
Type
Automatic Relational
Evidence-
based
Thematic
Results
#Discoveries #Rediscoveries
IRIDESCENT [108] Keyword 1 0
ARROWSMITH [84]
Keyword/Conc
ept
5 0
DAD [101,102] Concept 0 2
BITOLA [46] Concept 0 1
Litlinker [110] Concept 0 2
Manjal [87,88] Concept × 0 5
SemBT [40,41,42] Relations × × 0 1
BioSbKDS [47] Relations × × 0 1
Wilkowski [107] Graph × × 0 0
Ramakrishnan [72] Graph × × 0 1*
Zhang [114] Graph × × × 0 0
Obvio [19, 21] Graph × × × × 0 8
ARROWSMITH v2 [86,98] Hybrid × 0 6*
Semantic MEDLINE [18,63] Hybrid × × 2 0
Note: References are from the PhD Dissertation manuscript entitled: A Context Driven Subgraph Model for Literature-Based Discovery
Table 4: Comparison of capabilities and accomplishments of LBD techniques

53
Limitations
1. Manual Threshold
– MeSH Semantic Similarity
2. Path Relatedness Threshold
– Only Approximate Gaussian
3. Definition of Context

54
Levels of Semantic Representation
Keywords
Concepts
MeSH Descriptors
Semantic Predications
Ensemble of Features
Relationships
A B
Semantic Predication
PREDICATE

55
Limitations
1. Manual Threshold
– MeSH Semantic Similarity
2. Path Relatedness Threshold
– Only Approximate Gaussian
3. Definition of Context
4. MEDLINE Querying
– Deep integration of Assertional/Definitional
5. Contradiction Detection
6. Statistical Evaluation
7. Scalability of Clustering Algorithm
8. Subgraph Labeling

56
Take Away
• Future of Information Processing
– Rich Knowledge Representations
o Implicit, Formal, Powerful semantics
– Application to Literature-Based Discovery

57
Conclusion
• Context-Driven Subgraph Model
– Manually create Complex Associations
– Automatic Subgraph Creation
o Novel definitions for Context and Shared Context
o Multiple Thematic Dimensions
– Predications-based Knowledge Exploration
o Predicates
o Highlighted MEDLINE sentences
– Knowledge Rediscovery
o 8 out of 9 existing scientific discoveries

58
Publications
1. D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Context-Driven Automatic Subgraph Creation for
Literature-Based Discovery (under preparation)
2. D. Cameron, A. P. Sheth, N. Jaykumar, G. Anand, K. Thirunarayan, G. A. Smith. A Hybrid Approach to Finding Relevant Social Media Content for
Domain Specific Information Needs. (submitted to the Journal of Web Semantics)
3. D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery
and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13). 46(2): 238–251, 2013.
4. D. Cameron, G. A. Smith, R. Daniulaityte, A. P. Sheth, D. Dave, L. Chen, G. Anand, R. Carlson, K. Z. Watkins, R. Falck. PREDOSE: A Semantic Web
Platform for Drug Abuse Epidemiology using Social Media Journal of Biomedical Informatics (JBI13). 46(6): 985–997, 2013.
5. R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. “I just wanted to tell you that Loperamide WILL WORK: A Web-Based
Study of Extra-medical use of Loperamide. Journal of Drug and Alcohol Dependence (DAD13) 130(1–3): 241–244, 2013.
6. D. Cameron, V. Bhagwan, A. P. Sheth. Towards Comprehensive Longitudinal Healthcare Data Capture. International Workshop on Semantic Web
in Literature-Based Discovery (SWLBD12). 241–247, 2012.
7. R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. A Web-Based Study of Extra-medical use of Loperamide. The College on
Problems of Drug Dependence (CPDD12), 2012.
8. D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in
Biomedical Literature. International Bioinformatics and Biomedical Conference (BIBM11). 512–519, 2011.
9. D. Cameron, B. Aleman-Meza, I. B. Arpinar, S. L. Decker, A. P. Sheth. A Taxonomy-based Model for Expertise Extrapolation. International
Conference on Semantic Computing (ICSC10). 333–240, 2010.
10. D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference
(ACMSE10). 14, 2010.
11. C. Thomas, W. Wang, P. Mehra, D. Cameron, P. N. Mendes, A. P. Sheth. What Goes Around Comes Around – Improving Linked Open Data through On-
Demand Model Creation. Web Science Conference (WebSci10), 2010.
12. P. N. Mendes, P. Kapanipathi, D. Cameron, A. P. Sheth. Dynamic Associative Relationships on the Linked Data Web. Web Science Conference (WebSci10),
2010.

59
Research Expertise
Literature-Based
Discovery
Text MiningQuestion
Answering
[1]
Information
Retrieval
[2]
[3]
[6]
[4]
[8]
[10]
[5]
[7]

60
Parting Words
“...some day the piecing together of dissociated knowledge will open up such
terrifying vistas of reality,...that we shall either go mad from the revelation or
flee from the deadly light into the peace and safety of a new dark age.”
– H. P. Lovecraft (The Call of Cthulhu, The Horror in Clay).
H. P. Lovecraft. The Call of Cthulhu. In S. T. Joshi, editor. The Call of Cthulhu and Other Weird Stories. Penguin Books Ltd., London, 1999

61
Acknowledgements
• Olivier Bodenreider
• Marcelo Fiszman
• Mike Cairelli
• Swapna Abhyankar
• Drashti Dave
• Dongwook Shin
• Special Thanks
o Pavan
o Shreyansh
o Swapnil
o Nishita
• PREDOSE Team
o Nishita
o Gaurish
o Alan
o Revathy

62
Ph.D. Committee Members
Amit P. Sheth
(Advisor)
T.K. Prasad Michael Raymer
Ramakanth Kavuluru Thomas C. Rindflesch Varun Bhagwan

Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for Literature-based Discovery

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