Download A random walk based approach for improving interaction network

Chengwei LEI, Ph.D.
Assistant Professor of Computer Science
Department of Electrical Engineering and Computer Science
McNeese State University
What is Interaction Network
• Interaction network is a network of
nodes that are connected by features.
First Introduced in Biology
• If the feature is a physical and
molecular, the interaction network is
molecular interactions usually found
in cells.
Network View of
Protein Interaction Network
Sounds familiar?
Sounds familiar?
Even In Mechanical Engineering
Real-world Classification
• Noisy data
• Overfitting problem
• Few true “driver” changes / vast
number of “passenger” changes.
Good
Bad
Current Methods
Classifier
Prediction
Current Methods
Statistical test
Pick the most significant ones
Classifier
Prediction
Problem?
• Ignore the relationships between
nodes/features/sensors
Our approach
• Improve prognosis by combining
– Node readout data
– Node-node interaction networks
Classifier
Prediction
Network
Transformation
Matrix
Network
Transformation
Matrix
Network
Transformation
Matrix
Classifier
Prediction
Transformation Matrix
• Transformation matrix is generated by apply the
Random Walk with Restart (RWR) algorithm on the
Interaction network.
Random Walk
• A random walk is a mathematical formalization of
a path that consists of a succession of random
steps.
Random Walk
• A random walk is a mathematical formalization of
a path that consists of a succession of random
steps.
• Random walk for one node on a graph G is a walk
on G where the next node is chosen uniformly at
random from the set of neighbors of the current
node
– when the walk is at node v, the probability to move in
the next step to the neighbor u is Pvu = 1/d(v) for (v, u)
is connected and 0 otherwise.
Random Walk
Random Walk
Step 1
Random Walk
Random Walk
Step 2
Random Walk
Random Walk
Random Walk
Random Walk
Step 3
Random Walk
Step 1
Step 3
Step 2
…… Step N
Random Walk with Restart
• A random walker start from a node (v) with
– uniform probability to visit its neighbors
– fixed probability c to revisit the start node (v)
• The probability for a random walker to be on
node j after k times is
– fijk(v) is the probability for a random walker to
take path i to j at time k
– Fj(v) at equilibrium is the probability for a random walker
starting from node v to reach node j => Similarity between
patient v and j
How about Two?
Experiments
• Biology Data
– Cancer prediction
Classification results
Wang’s Dataset
Network
Transformation
Matrix
Wang’s Dataset
10144
10144
1
1
0
… 1
1
1
0
… 1
0
0
1
… 0
286
… … … … …
286
1
1
0
… 1
10144
7885
7885
7885
1
1
0
… 1
1
1
0
… 1
… … … … …
1
1
0
… 1
2259
286
286
Good
Bad
7885
7885
2259
T-test
1247
286
Good
Bad
286
7885
7885
2259
1678
T-test
286
286
7885
7885
2259
52
1195
483
Pvalue comparison for Wang’s data
Significantly
up-regulated
genes
Significantly
down-regulated
genes
For Vijver’s dataset
DE Genes
49
1463
856
Further verification
• For verification, search each gene in the PubMed database
– pick the top DE genes from the original dataset and the
enhanced dataset,
– with keyword “( GENE-NAME ) AND Cancer AND
(Metastasis or Metastatic) ”.
Top 15 DE genes in original dataset
Top 15 original non-significant
genes in the enhanced dataset
Top 15 original non-significant
genes in the enhanced dataset
• SLC26A8 is a male reproductive system
diseases related gene
• It is also related to breast cancer
Top 15 original non-significant
genes in the enhanced dataset
• SLC26A8 is a male reproductive system
diseases related gene
• It is also related to breast cancer
–
–
A. E. Dahm, A. L. Eilertsen, J.
Goeman, “A microarray study on
the effect of four hormone therapy
regimens on gene transcription in
whole blood from healthy
postmenopausal women,”
Thrombosis research, vol. 130, no.
1, pp. 45–51, 2012.
J.-H. Shin, E. Son, H. Lee, S. Kim,
“Molecular and functional
expression of anion exchangers in
cultured normal human nasal
epithelial cells,” Acta physiologica,
vol. 191, no. 2, pp. 99–110, 2007
Top 15 original non-significant
genes in the enhanced dataset
• RPS6 is a very important gene in cancer
research, especially for the cancer
antibodies drug development
Top 15 original non-significant
genes in the enhanced dataset
• RPS6 is a very important gene in cancer
research, especially for the cancer
antibodies drug development
–
–
J. C. Potratz, D. N. Saunders, D.
H. Wai, et al., “Synthetic lethality
screens reveal rps6 and mst1r as
modifiers of insulin-like growth
factor-1 receptor inhibitor activity in
childhood sarcomas,” Cancer
research, vol. 70, no. 21, pp.
8770–8781, 2010.
F. Henjes, C. Bender, S. von der
Heyde, L. Braun, H. et al., “Strong
egfr signaling in cell line models of
erbb2-amplified breast cancer
attenuates response towards
erbb2-targeting drugs,”
Oncogenesis, vol. 1, no. 7, p. e16,
Top 15 original non-significant
genes in the enhanced dataset
• G2E3 is a dual function ubiquitin ligase required for early
embryonic development
• and also a nucleo-cytoplasmic shuttling protein with DNA damage
responsive localization
Top 15 original non-significant
genes in the enhanced dataset
• G2E3 is a dual function ubiquitin ligase required for early
embryonic development
• and also a nucleo-cytoplasmic shuttling protein with DNA damage
responsive localization
–
W. S. Brooks, E. S. Helton, S.
Banerjee, “G2e3 is a dual function
ubiquitin ligase required for early
embryonic development,” Journal
of Biological Chemistry, vol. 283,
no. 32, pp. 22 304–22 315, 2008.
Top 15 original non-significant
genes in the enhanced dataset
• RACGAP1 plays a regulatory role in cell growth,
transformation and metastasis
Top 15 original non-significant
genes in the enhanced dataset
• RACGAP1 plays a regulatory role in cell growth,
transformation and metastasis
–
–
–
S. Saigusa, K. Tanaka, Y. Mohri,
M. Ohi, T. Shimura, et al., “Clinical
signif-icance of racgap1
expression at the invasive front of
gastric cancer,” Gastric Cancer,
pp. 1–9, 2014.
V. Kotoula, K. T. Kalogeras, G.
Kouvatseas, D. Televantou, R.
Kro-nenwett, “Sample parameters
affecting the clinical relevance of
rna biomarkers in translational
breast cancer research,” Virchows
Archiv, vol. 462, no. 2, pp. 141–
154, 2013.
K. Pliarchopoulou, K. Kalogeras,
R. Kronenwett, et al., “Prognostic
significance of racgap1 mrna
Top 15 original non-significant
genes in the enhanced dataset
Ongoing Experiment