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Association Pattern Analysis –
Applications in Bioinformatics
Vipin Kumar
University of Minnesota
[email protected]
www.cs.umn.edu/~kumar
Team Members: Michael Steinbach, Rohit Gupta, Hui Xiong, Gaurav Pandey, Blayne Field,
Meenal Chhabra, Beth Zirbes
Research supported by NSF, IBM
Data Mining for Bioinformatics
 Recent technological advances are helping to
generate large amounts of both medical and
genomic data
•
•
High-throughput experiments/techniques
- Gene and protein sequences
- Gene-expression data
- Biological networks and phylogenetic profiles
Electronic Medical Records
- IBM-Mayo clinic partnership has created a DB of 5
million patients
- Single Nucleotides Polymorphisms (SNPs)
 Data mining offers potential solution for
analysis of large-scale data
•
•
•
Automated analysis of patients history for customized
treatment
Prediction of the functions of anonymous genes
Identification of putative binding sites in protein
structures for drugs/chemicals discovery
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Association Pattern Analysis – Applications in Bioinformatics
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Protein Interaction Network
Association Analysis
• Association analysis: Analyzes
relationships among items
(attributes) in a binary transaction
data
– Example data: market basket data
– Applications in business and science
•
•
Marketing and Sales Promotion
Identification of functional modules from protein complexes
• Two types of patterns
– Itemsets: Collection of items
• Example: {Milk, Diaper}
– Association Rules: X  Y, where X
and Y are itemsets.
• Example: Milk  Diaper
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Set-Based Representation of Data
TID
Items
1
Bread, Milk
2
3
4
5
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
Support, s 
# transacti ons that contain X and Y
Total transacti ons
Confidence , c 
Association Pattern Analysis – Applications in Bioinformatics
# transacti ons that contain X and Y
# transacti ons that contain X
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I. Application of Association Analysis:
Identification of Protein Function Modules
 Proteins usually do not function in isolation. They
interact with other proteins either in pairs or as
components of large complexes
 Protein complexes can be determined using
large scale experimental studies
 Functional module is a group of proteins that is
involved in common elementary biological
function
 Association analysis techniques can be used for
identification of functional modules from a
collection of protein complexes
Protein Complex Data
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Protein Complexes
Proteins
c1
p1, p2
c2
p1, p3, p4, p5
c3
p2, p3, p4, p6
Association Pattern Analysis – Applications in Bioinformatics
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II. Application of Association Analysis:
Personalized Medicine
• Given: Patient data set that records
– Phenotypic characteristics
– Genetic characteristics (SNPs)
– Disease
• Objective: Find relationships between disease and medical
and genomic characteristics
• Association analysis can be used to find groups of
phenotypic and genetic characteristics that are highly
associated with disease
Recently started project in collaboration with IBM Rochester – Drew Flaada, Fred Kullack, Tim
Mullins, Carl Oberto
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Association Pattern Analysis – Applications in Bioinformatics
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III. Application of Association Analysis: Protein
Function Prediction Using Phylogenetic Profiles
• Phylogenetic profiles:
– For a given protein, BLAST its sequence
against N sequenced genomes
– Construct a vector with N coordinates s.t.
if a protein has a homolog in the organism
n, set coordinate n to 1, Otherwise set it
to 0
• Basic Idea: If two proteins, P1 and
P2 function/interact together, they
must co-evolve. So every organism
that has a homolog of P1 must also
have a homolog of P2
• Association techniques can be used
to identify the protein groups and the
functional linkages among them with
the help of phylogenetic profiles
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Association Analysis

Process of finding interesting patterns:
• Find frequent itemsets using a support threshold
• Find association rules for frequent itemsets
• Sort association rules according to confidence

Support filtering is necessary
• To eliminate spurious patterns
• To avoid exponential search
-

A
B
C
D
E
AB
AC
AD
AE
BC
BD
BE
CD
CE
DE
ABC
ABD
ABE
ACD
ACE
ADE
BCD
BCE
BDE
CDE
Support has anti-monotone property:
X  Y implies (Y) ≤ (X)
ABCD
Confidence is used because
of its interpretation as
conditional probability
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null
ABCE
ABDE
ACDE
BCDE
ABCDE
Given d items, there are 2d
possible candidate itemsets
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Drawback of Confidence
Ref: Brin, Motwani, SIGMOD-97
Coffee
Coffee
Tea
15
5
20
Tea
75
5
80
90
10
100
Association Rule: Tea  Coffee
Confidence= P(Coffee|Tea) = 0.75
but P(Coffee) = 0.9
 Although confidence is high, rule is misleading
 P(Coffee|Tea) = 0.9375
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There are lots of
measures proposed
in the literature
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Comparing Different Measures
10 examples of
contingency tables:
Rankings of contingency tables
using various measures [4] Tan et al:
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Example
f11
E1
E2
E3
E4
E5
E6
E7
E8
E9
E10
8123
8330
9481
3954
2886
1500
4000
4000
1720
61
f10
f01
f00
83
424 1370
2
622 1046
94
127 298
3080
5
2961
1363 1320 4431
2000 500 6000
2000 1000 3000
2000 2000 2000
7121
5
1154
2483
4
7452
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Hyperclique Pattern
 The h-confidence of a pattern P = {i1, i2,…, im}
 Example:
For a pattern P = {A,B,C}, assume that:
•
supp({A}) = 0.1, supp({B}) = 0.1, supp({C}) = 0.06, supp({A,B,C}) = 0.06
•
hconf(A,B,C) = [supp({A,B,C})] / [max{supp({A}),supp({B}),supp({C})}]
= 0.06/0.1 = 0.6
 A pattern P is a hyperclique pattern if hconf(P)>=hc, where
hc is a user specified minimum h-confidence threshold
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Alternate Equivalent Definitions of hconfidence
 Given a pattern P = {i1, i2,…, im}
• Definition:
hconf ( P)  min{conf ({x}  {P  {x}}) | x {i1 , i2 ,..., im}}
• Definition:
hconf ( P)  min{conf ( X  Y ) | X , Y  {i1 , i2 ,..., im}& X  Y  P}
All-Confidence Measure
Omiecinski – TKDE 2003
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Properties of Hyperclique Pattern
 Anti-monotone
if P '  P, then hconf ( P ')  hconf ( P)
 High Affinity Property
• High h-confidence implies tight coupling amongst all items in the pattern
 Cross support property
• eliminates patterns involving items that have very different support levels
 Magnitude of relationship consistent with many other
measure
• Jaccard, Correlation, Cosine
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Cross Support Property of h-confidence
 At high support, all patterns that
involve low support items are eliminated
At low support, too many spurious
patterns are generated that involve one
high support item and one low support
item
 Given a Pattern P = {i1, i2,…, im}
 For any two Itemsets X , Y  P
Support distribution of the pumsb dataset
X Y  P & X Y  
hconf(P)
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
supp{X}
supp{Y}
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Consistency with other Measures
 Jaccard
• If an item set P = {i1,i2} is a size-2 hyperclique pattern, then jaccard ( P)  hc / 2
 Correlation
• Let S be a set of items and hc be the minimum h-confidence, we can
form two groups: S1  {x | sup p({x})  hc & x  S}, S 2  {S  S1}
Then, any size-2 hyperclique pattern P = {A,B} has a positive correlation
in each of the following cases:
 Cosine
Case1: A  S1& B  S 2
Case2 : A  S1& B  S1
• If an item set P = {i1,i2} is a size-2 hyperclique pattern, then cos ine( P)  hc
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Protein Complex Data
Protein Complexes
(Higher-order Functions)
Functional Modules
(Elementary Functions)
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 The TAP-MS dataset by Gavin et al 2002:
Tandem affinity purification (TAP) – mass
spectrometry (MS)
 Contains 232 multi-protein complexes
formed using 1361 proteins
 Number of proteins per complex range
from 2 to 83 (average 12 components)
 Protein complex data is incomplete and
noisy
Protein Complexes
Proteins
c1
p1, p2
c2
p1, p3, p4, p5
c3
p2, p3, p4, p6
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Functional Group Verification Using
Gene Ontology
 Hypothesis: Proteins within the same
pattern are more likely to perform the
same function and participate in the
same biological process
 Gene Ontology
• Three separate ontologies:
Biological Process, Molecular
Function, Cellular Component
• Organized as a DAG describing
gene products (proteins and
functional RNA)
• Collaborative effort between
major genome databases
http://www.geneontology.org
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Hyperclique Patterns from Protein
Complex Data
 List of maximal hyperclique patterns at a support threshold 2 and an h-confidence
threshold 60%. [1] Xiong et al. (Detailed results are at http://cimic.rutgers.edu/~hui/pfm/pfm.html)
2 Tif4632 Tif4631
2 Cdc33 Snp1
2 YHR020W Mir1
2 Cka1 Ckb1
2 Ckb2 Cka2
2 Cop1 Sec27
2 Erb1 YER006W
2 Ilv1 YGL245W
2 Ilv1 Sec27
2 Ioc3 Rsc8
2 Isw2 Itc1
2 Kre33 YJL109C
2 Kre33 YPL012W
2 Mot1 Isw1
2 Npl3 Smd3
2 Npl6 Isw2
2 Npl6 Mot1
2 Rad52 Rfa1
2 Rpc40 Rsc8
2 Rrp4 Dis3
2 Rrp40 Rrp46
2 Cbf5 Kre33
3 YGL128C Clf1 YLR424W
3 Cka2 Cka1 Ckb1
3 Has1 Nop12 Sik1
3 Hrr25 Enp1 YDL060W
3 Hrr25 Swi3 Snf2
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3 Kre35 Nog1 YGR103W
3 Krr1 Cbf5 Kre33
3 Nab3 Nrd1 YML117W
3 Nog1 YGR103W YER006W
3 Bms1 Sik1 Rpp2b
3 Rpn10 Rpt3 Rpt6
3 Rpn11 Rpn12 Rpn8
3 Rpn12 Rpn8 Rpn10
3 Rpn9 Rpt3 Rpt5
3 Rpn9 Rpt3 Rpt6
3 Brx1 Sik1 YOR206W
3 Sik1 Kre33 YJL109C
3 Taf145 Taf90 Taf60
4 Fyv14 Krr1 Sik1 YLR409C
4 Mrpl35 Mrpl8 YML025C Mrpl3
4 Rpn12 Rpn8 Rpt3 Rpt6
6 Dim1 Ltv1 YOR056C YOR145C Enp1 YDL060W
6 Luc7 Rse1 Smd3 Snp1 Snu71 Smd2
6 Pre3 Pre2 Pre4 Pre5 Pre8 Pup3
7 Clf1 Lea1 Rse1 YLR424W Prp46 Smd2 Snu114
7 Pre1 Pre7 Pre2 Pre4 Pre5 Pre8 Pup3
7 Blm3 Pre10 Pre2 Pre4 Pre5 Pre8 Pup3
8 Clf1 Prp4 Smb1 Snu66 YLR424W Prp46 Smd2 Snu114
8 Pre2 Pre4 Pre5 Pre8 Pup3 Pre6 Pre9 Scl1
10 Cdc33 Dib1 Lsm4 Prp31 Prp6 Clf1 Prp4 Smb1 Snu66 YLR424W
12 Dib1 Lsm4 Prp31 Prp6 Clf1 Prp4 Smb1 Snu66 YLR424W Prp46
Smd2 Snu114
12 Emg1 Imp3 Imp4 Kre31 Mpp10 Nop14 Sof1 YMR093W YPR144C
Krr1 YDR449C Enp1
13 Ecm2 Hsh155 Prp19 Prp21 Snt309 YDL209C Clf1 Lea1 Rse1
YLR424W Prp46 Smd2 Snu114
13 Brr1 Mud1 Prp39 Prp40 Prp42 Smd1 Snu56 Luc7 Rse1 Smd3
Snp1 Snu71 Smd2
39 Cus1 Msl1 Prp3 Prp9 Sme1 Smx2 Smx3 Yhc1 YJR084W Brr1
Dib1 Ecm2 Hsh155 Lsm4 Mud1 Prp11 Prp19 Prp21 Prp31 Prp39
5 Ada2 Gcn5 Rpo21 Spt7 Taf60
Prp40 Prp42 Prp6 Smd1 Snt309 Snu56 Srb2 YDL209C Clf1 Lea1
6 YLR033W Ioc3 Npl6 Rsc2 Itc1 Rpc40 Luc7 Prp4 Rse1 Smb1 Smd3 Snp1 Snu66 Snu71 YLR424W
5 Rpn6 Rpt2 Rpn12 Rpn3 Rpn8
Association Pattern Analysis – Applications in Bioinformatics
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Summary
 Number of hypercliques:
• Size-2: 22, Size-3: 18, Size-4: 3, Size-5: 2
• Size-6: 4, Size-7: 3, Size-8: 2, Size-10: 1
• Size-12: 2, Size-13: 2, Size-39: 1
 In most cases, proteins identified as hypercliques found
to be functionally coherent and part of same biological
process evaluated using GO hierarchies
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Association Pattern Analysis – Applications in Bioinformatics
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Function Annotation for Hyperclique
{PRE2 PRE4 PRE5 PRE6 PRE8 PRE9 PUP3 SCL1}
 GO hierarchy
shows that the
identified proteins
in hyperclique
perform the same
function and
involved in same
biological process
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Association Pattern Analysis – Applications in Bioinformatics
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More Hyperclique Examples
# distinct proteins in cluster = 13
# proteins in one group = 12
(rest denoted as )
# distinct proteins in cluster = 13
# proteins in one group = 10
(rest denoted as
August 07, 2006
)
Association Pattern Analysis – Applications in Bioinformatics
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More Hyperclique Examples..
# distinct proteins in cluster = 12
# proteins in one group = 12
# distinct proteins in cluster = 8
# proteins in one group = 8
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Association Pattern Analysis – Applications in Bioinformatics
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More Hyperclique Examples..
# distinct proteins in cluster = 12
# proteins in one group = 12
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Association Pattern Analysis – Applications in Bioinformatics
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More Hyperclique Examples..
# distinct proteins in cluster = 10
# proteins in one group = 9
(rest denoted as
August 07, 2006
)
Association Pattern Analysis – Applications in Bioinformatics
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More Hyperclique Examples..
# distinct proteins in cluster = 39
# proteins in one group = 32
# proteins at node ‘mRNA splicing’ = 37
 Only two Proteins
SRB2 and ECM2
involved in cellular
process and
development got
clustered together
with group of
proteins involved in
physiological process
 It is observed that 37
proteins out of 39
annotated proteins
are responsible for
same molecular
function, mRNA
splicing via
spliceosome
August 07, 2006
Association Pattern Analysis – Applications in Bioinformatics
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Functional Annotation of Uncharacterized
Proteins
Hyeperclique Pattern: {Emg1 Imp3
Imp4 Kre31 Mpp10 Nop14 Sof1 YMR093W
YPR144C Krr1 YDR449C Enp1}
8 of the 12 proteins have
annotation of “RNA binding”
Other 4 proteins have no
functional annotation
Hypothesis: Unannotated
proteins have same molecular
function “RNA binding”, since
hypercliques tend to have
proteins that are functionally
coherent
August 07, 2006
Association Pattern Analysis – Applications in Bioinformatics
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Identification of Functional Modules Using
Frequent Itemset-based Approach
 Closed frequent itemset-based approach produces over 500 patterns of size 2 or
more with support threshold of 2
 Number of patterns
• for (h-confidence < 0.20) = 198
• Generally very poor
• for (0.20 <= h-confidence < 0.50) = 246
• moderate quality
• for (h-confidence >= 0.50) = 65
• Generally very good
 Proteins in large size patterns (with high h-confidence) are found to be better
functionally related than even proteins in small size patterns (with less hconfidence)
 2 examples illustrating this are shown
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Association Pattern Analysis – Applications in Bioinformatics
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Frequent Itemsets-based Results – GO
Hierarchies
# distinct proteins = 8
# proteins in one group = 8
h-confidence = 0.67
Support = 4
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Association Pattern Analysis – Applications in Bioinformatics
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Frequent Itemsets-based Results – GO
Hierarchies
# distinct proteins = 9
# proteins in one group = 5
h-confidence = 0.19
Support = 3
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Association Pattern Analysis – Applications in Bioinformatics
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Clustering of Protein Complex Data
 Clustering software CLUTO
(http://glaros.dtc.umn.edu/gkhome/views/cluto) is used to cluster the
proteins in groups
• Repeated bisection method is used as the base
method for clustering
• Cosine similarity measure is used to find similarity
between proteins
 Parameter to define the maximum number of
clusters that could be obtained is set to 100
 Best clusters (as measured by internal similarity)
are usually the candidates for functional
modules
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Clustering Results Summary
 Clusters with high internal similarity (as
ranked by Cluto program) and relatively
small sizes are found to be functionally
coherent using GO hierarchies
 It is found that large clusters with relatively
low internal similarity have proteins with
multiple function annotations
 Few examples to illustrate this are shown
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Association Pattern Analysis – Applications in Bioinformatics
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Clustering Results – GO
Hierarchies
# distinct proteins in cluster = 5
# proteins in one group = 5
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Association Pattern Analysis – Applications in Bioinformatics
# distinct proteins
in cluster = 6
# proteins in one
group = 6
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Clustering Results – GO
Hierarchies
 Proteins MNN10
and ANP1
(denoted by )
involved in
metabolism got
clustered
together with
group of proteins
involved in
physiological
process
# distinct proteins in cluster = 6
# proteins in one group = 4
August 07, 2006
Association Pattern Analysis – Applications in Bioinformatics
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Clustering Results – GO
Hierarchies
 Protein SKN1
(denoted by )
involved in
metabolism got
clustered
together with
proteins involved
in cellular
physiological
process
# distinct proteins in cluster = 11
# proteins in one group = 10
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Association Pattern Analysis – Applications in Bioinformatics
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Clustering Results – GO
Hierarchies
# distinct proteins in cluster = 7
# proteins in one group = 4
(Rest of the 3 proteins are marked
as )
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Association Pattern Analysis – Applications in Bioinformatics
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Clustering Results – GO
Hierarchies
 Protein AAP1
and VAM6
(denoted by )
got clustered
together with
group of
proteins
involved in
biological
process of
membrane
fusion
# distinct proteins in cluster = 8
# proteins in one group = 4
(rest denoted by
August 07, 2006
)
Association Pattern Analysis – Applications in Bioinformatics
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Error Tolerant Itemsets (ETIs)
• An error-tolerant itemset (ETI) can have a fraction
 of the items missing in each transaction.
Example: see the data in the table
– Let  = 1/4. In other words, each
transaction needs to have
3/4 (75%) of the items.
– X = {i1, i2, i3, i4} and
Y = {i5, i6, i7, i8} are both
ETIs with a support of 4
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Association Pattern Analysis – Applications in Bioinformatics
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ETIs to Identify Protein Functional Modules
 Groups of proteins are identified as error tolerant
itemsets (ETIs)
 ETI relaxes the density constraints of the pattern
in both dimensions
 Maximum sparseness allowed: 0.2 (along row)
and 0.25 (along column)
 Minimum support: 5 protein complexes
 Gene Ontology is used to validate following
three identified ETIs
• {CLF1,LEA1,PRP4,PRP46,RSE1,SMB1,SMD2,SNU114,SPP382}
• {Pre2,Pre4,Pre5,Pre6,Pre8, Pre9,Pup3,Rpt3,Scl1}
• {Rpn10,Rpn12,Rpn3,Rpn6,Rpn8,Rpn9,Rpt2,Rpt3,Rpt6}
August 07, 2006
Association Pattern Analysis – Applications in Bioinformatics
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ETI Pattern validated using GO
 Pattern: {CLF1,
LEA1, PRP4,
PRP46, RSE1,
SMB1, SMD2,
SNU114,
SPP382}
 Almost all
proteins involved
in one biological
process (mRNA
splicing)
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Association Pattern Analysis – Applications in Bioinformatics
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More ETI Patterns..
 Pattern:
{Pre2,Pre4,Pre5,Pre6,Pre8,
Pre9,Pup3,Rpt3,Scl1}
 All proteins involved in one
biological process, ubiquitindependent protein
catabolism
 Hyperclique technique
identified the same pattern
except protein RPT3, which
is found to have same
function – relaxing the
constraints using ETI
technique helped identify
bigger group
August 07, 2006
Association Pattern Analysis – Applications in Bioinformatics
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More ETI Patterns..
 Pattern: {Rpn10, Rpn12,
Rpn3, Rpn6, Rpn8, Rpn9,
Rpt2, Rpt3, Rpt6}
 All proteins involved in
one biological process,
ubiquitin-dependent
protein catabolism
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Concluding Remarks
 Hyperclique and ETI patterns show great
promise for identifying protein modules and for
annotating uncharacterized proteins
 Clustering does not perform as well as
hypercliques and ETI due to a variety of
reasons:
• Each protein gets assigned to some cluster even if
there is no right cluster for it
• Modules can be overlapping
• Modules can of different sizes
• Data is high-dimensional
August 07, 2006
Association Pattern Analysis – Applications in Bioinformatics
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References
[1] Hui Xiong, X. He, Chris Ding, Ya Zhang, Vipin Kumar, Stephen R. Holbrook, Identification of
Functional Modules in Protein Complexes via Hyperclique Pattern Discovery, in Proc. of the Pacific
Symposium on Biocomputing, (PSB 2005), 2005
[2] Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison-Wesley
April 2005
[3] Jinze Liu, Susan Paulsen, Xing Xu, Wei Wang, Andrew Nobel, Jan Prins, Mining Approximate
Frequent Item sets in the Presence of Noise: Algorithms and Analysis, SIAM 2006
[4] Pang-Ning Tan, Vipin Kumar, and Jaideep Srivastava, Selecting the Right Interestingness Measure
for Association Patterns, Proc of the Eighth ACM SIGKDD Int'l Conf. on Knowledge Discovery and
Data Mining (SIGKDD-2002)
[5] Hui Xiong, Pang-Ning Tan, and Vipin Kumar, Hyperclique Pattern Discovery, Data Mining and
Knowledge Discovery Journal, accepted for publication as a regular paper, 2006 (short version
appeared in proc of ICDM 2003)
[6] A. Gavin et al. Functional organization of the yeast proteome by systematic analysis of protein
complexes, Nature, 415:141-147, 2002
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Organizing Committee
http://www.siam.org/meetings/sdm07/
Steering Committee Chair
Vipin Kumar, University of
Minnesota
Conference Co-Chairs
Chid Apte, IBM Research
David Skillicorn, Queen’s University
Program Co-Chairs
Srinivasan Parthasarathy, Ohio
State University
Bing Liu, University of Illinois at
Chicago
Tutorial Chair
Pang-Ning Tan, Michigan State
University
Workshop Co-Chairs
Michael Berry, University of
Tennessee
Philip Chan, Florida Institute of
Technology
Publicity Chair
Hui Xiong, Rutgers University
August 07, 2006
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Association Pattern Analysis – Applications in Bioinformatics
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