Download Graph and Topological Structure Mining on Scientific Articles

Graph and Topological Structure
Mining on Scientific Articles
Fan Wang, Ruoming Jin,
Gagan Agrawal and Helen Piontkivska
The Ohio State University
The Kent State University
Presenter: Fan Wang
The Ohio State University
Outline
• Introduction
• Topological Structure Mining
• Data Preprocessing and Graph
Representations
• Experiment Results and Pattern Analysis
• Conclusion
Introduction
• Huge number of genes in literature
• Associated with targeted disease or
functionality
• Finding interaction among genes manually
– Time consuming
– Error Prone
Introduction
CCL5
CCR5
CCL3
CCL5
T
CCL4
CCL4
CCL3
T
CD4
• Well-known relationship among chemokine ligands
• Mining these relations from literature documents
• Mining frequent patterns from graph datasets
– Convenient representation
– Lots of research in subgraph mining
Introduction
• Our Goal
– Find commonly occurring interactions
– Represent them visually
• Capture the co-occurrence of scientific
terms
• Graph representation of scientific document
• Mining frequent topological structures
Outline
• Introduction
• Topological Structure Mining
• Data Preprocessing and Graph
Representations
• Experiment Results and Pattern Analysis
• Conclusion
Topological Structure Mining
• Disadvantages of subgraph mining
– Exact matching
– Missing potential patterns
• Focusing on the topological relationship
• Incorporating approximate matching
Topological Structure Mining
G
Y
X
G is a subgraph of Y
X is a (0,3) topological structure of Y
Topological Structure Mining
• Definition
– Given a collection of graphs, two parameters l
and h, and a threshold θ. A (l,h)-topological
structure whose support is greater than or
equal to θis called a frequent topological
structure.
• Given a set of graphs, in our KDD05 paper,
an algorithm TSMiner finding frequent
topological structures is implemented
Our Work
• Using topological structure mining
• Challenges
– How to create graphs?
– What are the keywords?
– How to insert edges into graphs?
Outline
• Introduction
• Topological Structure Mining
• Data Preprocessing and Graph
Representations
• Experiment Results and Pattern Analysis
• Conclusion
Data Preprocessing
and Graph Representation
• One graph for each document
• Nodes are keywords of interest
• Edges inserted based on occurrence of the
keywords
• Run topological structure mining algorithm
Data Preprocessing
• Four dictionaries of keywords
– Short Dictionary
• 321 genes expressed between prostate epithelial
and stromal cells
– Long Dictionary
• 2600 human genes found in supperarray’s DNA
microarray experiment
– Confusion Dictionary
• Gene names easily confused with ordinary words
– GO Dictionary
• GO terms (molecular function, biological process
and cellular component)
Graph Representations
• Edge Construction Methods
– Sentence-based Method
• Two keywords in one sentence
– Mutual Information Method
• The mutual information of two keywords greater than a
threshold
– Sliding Window Method
• Two keywords located within a sliding window with a predefined size
Outline
• Introduction
• Topological Structure Mining
• Data Preprocessing and Graph
Representations
• Experiment Results and Pattern Analysis
• Conclusion
Experiment Results
• Focusing on articles containing at least
one of the 5 genes
– CCL5, TF, IGF1, MYLK, IGFBP3
• Generating graph for each article
• Finding frequent topological structures
Three Edge Construction Methods
Number of Patterns Found, MYLK, sup=20%
Number of Patterns Found, MYLK, sup=15%
1000
800
500
Sentence
MI
Window
Total Number of Patterns
Total Number of Patterns
1200
600
400
200
0
400
Sentence
MI
300
Window
200
100
0
(0,0)
(1,1)
L and H values
(2,2)
(0,0)
(1,1)
L and H values
(2,2)
Three Edge Construction Methods
Number of Patterns Found, IGFBP3, sup=20%
Number of Patterns Found, IGFBP3, sup=15%
350
300
Sentence
MI
250
Window
350
Total Number of Patterns
Total Number of Patterns
400
200
150
100
50
300
250
200
Sentence
MI
Window
150
100
50
0
0
(0,0)
(1,1)
L and H values
(2,2)
(0,0)
(1,1)
L and H values
(2,2)
Three Edge Construction Methods
Results
• Sliding window method wins
– Largest number of frequent patterns
– Best scalability
• Topological structure mining giving us
more frequent patterns
• Large number doesn’t mean high
biological significance
Pattern Analysis
CCL4
CCL3
T
CCR5
CCL3
T
CCL4
PSMB6
CCL3
•
CCBP2
CCL5
CCL5
CCL4
• ONLY be found by
CCL5
CCL5
T
CCL4
•
T
CCL3
CXCR4
PSMB6
Underlying Graph 1
PSMB6
Underlying Graph 2
topological structure
mining
ONLY be found by
sliding window method
Restoring nodes
revealing interesting
patterns
Outline
• Introduction
• Topological Structure Mining
• Data Preprocessing and Graph
Representations
• Experiment Results and Pattern Analysis
• Conclusion
Conclusion
• Sliding window method is the best
– The most number of frequent patterns
– The highest quality of frequent patterns
• Topological structures found
corresponding well to known relationships
• Topological mining being a very valuable
tool for biological researchers
Three Edge Construction Methods
• Interestingness of Edges
– Counting the number of distinct edges
– Computing the average interestingness of edges for
all patterns found by using each edge construction
method