Presented at the 3rd International Conference on Personalized Medicine, June 26-29, 2014. Dr. Gangopadhyay is Chair of the Department of Information Systems at University of Maryland Baltimore County.

1. Data
Analy)cs
for
Personalized
Medicine
Aryya
Gangopadhyay
UMBC
Presented
at
the
3rd
Interna7onal
Conference
on
Personalized
Medicine,
June
26-­‐29,
2014

2. Scope
• Big
data
promise
(Pentland
et
al
2013)
– US
Healthcare
industry
can
save
$200
billion
per
year
• Need
complete
picture
– Reality
mining
(MIT
Tech.
Review
2008)
– Socio-­‐demographics
– EMRs
– Biological
data
• Interac7ons
in
the
network
– Topology-­‐based
analysis
– Centrality-­‐based
analysis
– Perturba)ons
(diseases
as
network
perturba)ons:
del
Sol
et
al
2010)
• Network
par77oning
• Visualiza7on

3. • “Within
10
years
every
healthcare
consumer
will
be
surrounded
by
a
virtual
cloud
of
billions
of
data
points”
[Hood
et
al.
2013]
Big
data
in
healthcare

4. Interconnec)ons
– Biological
processes
are
interconnected
systems
– Analyze
interac)ons
– Resilient
against
random
perturba)ons
– Vulnerable
to
targeted
aXacks
CIDeR:
Large,
mul7-­‐dimensional,
mul7modal,
dynamic

5. Extensions
to
our
previous
work
– Updated
the
network
• Nodes:
5168
to
9767
• Edges:
14410
to
27744
– Previous
analysis
• Network
characteris)cs:
CC,
diameter,
path
lengths,
etc.
• Node-­‐based
analysis
– Developed
a
new
method
for
iden)fying
effectors
and
receptors
• Perturba)on
analysis
– Extensions
• How
do
we
par))on
the
network?
• What
criteria
to
use
and
why?
• What
are
the
effects
of
such
par))oning?

6. Network
extracted
from
CIDeR:
2014
• Nodes:
9767
• Edges:
27744
• Diameter:
15
• #
CC:
89
• Avg.
PL:
4.7
• Avg.
degree:
2.8

7. Node
Centrality
measures:
correla)ons
x
=
Authority
Y
=
Betweenness
Centrality
Correla)on:
0.8
x
=
Clustering
Coefficient
Y
=
Betweenness
Centrality
Correla)on:
-­‐0.02
x
=
Hub
Y
=
Authority
Correla)on:
0.88
x
=
PageRank
Y
=
Authority
Correla)on:
0.92

8. Correla)ons
of
Node
Centrality
measures
Clustering.Coefficient
Clustering.Coefficient
Hub
Hub
Authority
Authority
PageRank
PageRank
Eigenvector.Centrality
Eigenvector.
Centrality
Betweenness.Centrality
Betweenness.
Centrality
Eccentricity
Eccentricity

9. Overall
network
characteris)cs
• PageRank,
hub
and
authority
scores
are
strongly
correlated
• Clustering
coefficient
is
nega)vely
correlated
with
other
node
centrality
measures
• Implica7ons:
1. Nodes
that
are
strong
effectors
are
also
strong
receptors
2. Less
central
nodes
are
not
connected
to
each
other
but
mainly
with
an
influen)al
node
3. Influen7al
nodes
are
mostly
connected
to
each
other
4. Fully
connected
sub-­‐graphs
are
small
and
rare

10. Par))oning
the
graph
• How
can
we
capture
the
above
characteris)cs?
• Modularity:
• The
objec)ve
is
to
maximize
Q
• Intui)on:
– Put
influen)al
nodes
in
separate
clusters
– Create
dense
sub-­‐communi)es
(common
neighbors)
• Algorithms
(op)mal
solu)on
is
NP-­‐hard:
Brandes
2007):
– Spectral
clustering
based
(Newman
2006)
– Greedy
algorithm
(Blondel
et
al.
2008)
Q =
1
2m
(Aij −
didj
2m
)
i∈Cl , j∈Cl
∑
l=1
k
∑

11. Clusters
formed
by
maximizing
modularity

12. Dendrogram
of
top
8
Disease
Clusters
C
C

13. Cluster
100
Nodes:
1177
Edges:
2122

14. Cluster
82
Nodes:
1200
Edges:
2554

15. K-­‐core
• Objec7ve:
Restrict
analysis
to
regions
of
increased
centrality
and
connectedness
• K-­‐core:
largest
sub-­‐graph
where
all
nodes
have
a
minimum
degree
of
k
(Batagelj
2002).
• K=5
(mode=2
for
the
en)re
network)
• Protein
Interac)on
Networks
(Wuchty
et
al
2005,
Hamelin
et
al
2008)
Taken
from
Hamelin
et
al
2008

16. 5-­‐core
graph:
color
code-­‐Type

17. 5-­‐core
graph:
color-­‐code:
Modularity
class

18. Disease
Clusters
(top
5)
dendrogram
C
C

19. 5-­‐core
graph:
Cluster
5
(26%)

20. 5-­‐core
graph:
Cluster
6
(22%)

21. 5-­‐core
graph:
Cluster
0
(16%)

22. 5-­‐core
graph:
Cluster
3
(13%)

23. 5-­‐core
graph:
Cluster
4
(12.5%)

24. Comparison
of
clusters

25. • Contribu7ng
areas
• Biology,
bioinforma)cs,
sociology,
SNA,
Physics,
applied
mathema)cs,
Computer
and
informa)on
sciences
• Summary
• Holis)c
analysis
of
health
data
• Analysis
based
on
node
centrality
• Network
par))oning
• Studying
the
effect
of
perturba)on
• Where
do
we
go
from
here
• Create
a
taxonomic
structure
of
elements
and
interac)ons
• Search
tool
• Biological
and
clinical
implica)ons
Conclusion