University at Buffalo The State University of New York Clustering of Interaction Clustering of Interaction Network Network Definition Process to detect densely connected sub-graphs Determines protein complexes or functional modules Difficulties Noisy data (too many false positives or false negatives) Cannot be solved by traditional clustering techniques Difficult to define the pair-wise distance between proteins in the network. Protein complexes may overlap. Disparate sources of data Different reliabilities 17%~50% Small overlaps <17%
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University at BuffaloThe State University of New York Clustering of Interaction Network Definition qProcess to detect densely connected sub-graphs qDetermines.
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University at Buffalo The State University of New York
Clustering of Interaction NetworkClustering of Interaction Network
Definition
Process to detect densely connected sub-graphs
Determines protein complexes or functional modules
Difficulties
Noisy data (too many false positives or false negatives) Cannot be solved by traditional clustering techniques
Difficult to define the pair-wise distance between proteins in the network.
Protein complexes may overlap. Disparate sources of data
Different reliabilities 17%~50%
Small overlaps <17%
University at Buffalo The State University of New York
Protein Interaction NetworkProtein Interaction Network Undirected, unweighted graph
University at Buffalo The State University of New York
Application Virus Spread Simulation on the road network at the city of Oldenburg, German
Green edges: Healthy edges Red edges: Damaged edges by spread process Blue edges: Damaged edges by defense process
Uncontrolled = 0.02
Intermediate = 0.12
Controlled = 0.22
University at Buffalo The State University of New York
Osteoporosis Osteoporosis
Definition: “a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue leading to enhanced bone fragility and a consequent increase in fracture risk”
25 million people in the United States are suffered. $10 billion dollars are expended by medical charges including
rehabilitation and treatment facilities. Research Funding will be $200 billion by the year of 2040
Normal Osteoporosis
University at Buffalo The State University of New York
Challenges Diagnosis of Osteoporosis?
Traditional method of evaluating bone strength is by assessing bone mineral density (BMD).
Limitations on BMD A major limitation of BMD is that it incompletely reflects
variation in bone strength. Other factors like bone microarchitecture contribute
substantially to bone strength By evaluating bone microstructure we can improve
determination of bone quality and strength
Computational Model on Bone Microstructure
University at Buffalo The State University of New York
Computational Model on Bone Microstructure
Questions What is the better way to evaluate bone strength? How can we identify fragile locations of the bone structure? Why don’t we think this problem in a new direction?
Let me think this problem with the structural point of view.
Graph-based approach of bone microstructure Bone microstructure contributes on bone strength. We suppose rod-like mineral fibers represented by edges in a
graph. It is capable of quantitative
assessment of bone mineral
density and bone micro-architecture
University at Buffalo The State University of New York
Model Approach
Bone is not a uniformly solid material, but rather has some spaces between its hard elements.
Designing a network approach model for the bone microstructure.
Quantitative assessment of bone mineral density could be successfully done with this approach.
University at Buffalo The State University of New York
Bone Network Model Creating Bone Network
A femur bone image from patients with osteoporosis by DXA scan.
By image profiling on DXA scan image, we create bone network based on the bone density.
What represent nodes and edges in bone network model? Node: fiber binding point for bone
cell movements and biochemical interactions
Edge: a group of mineralized fibers Weight of nodes and edges
Node weight: average weight of directly connected edges
Edge weight: Strength status of mineralized fibers
University at Buffalo The State University of New York
Problem / Solution Approach
What alternative ways for determining the strength of bone rather than Bone Mineral Density (BMD)?Designing a computational
model of bone microstructure.
How can we identify fragile locations of the bone structure?Creating algorithms for
mining weak locations from a computational model of bone microstructure.
Bone Model
Human Bone
University at Buffalo The State University of New York
Identifying Critical Locations
Information Propagation ModelAn algorithm to find critical edges
in bone networkMeasuring the quantity of stress
energy in each edgeCutting the most critical edge by
Information Propagation Model Iteratively run to find the next
critical edges. It stops at the first isolated network
University at Buffalo The State University of New York
Conclusions
Various applications are generating data very rapidly and in great volume, demanding data mining approaches.
Network-based approaches look promising to solve complex problems.
This research requires close collaboration among multidisciplinary groups.
Semi-supervised approaches to integrate domain knowledge into data mining tools are important to the success of the research.