Download Association Analysis-based Pre-processing of Protein Interaction

Association Analysis-based Extraction
of Functional Information from
Protein-Protein Interaction Data
Vipin Kumar
University of Minnesota
[email protected]
www.cs.umn.edu/~kumar
Team Members: Michael Steinbach, Rohit Gupta, Hui Xiong, Gaurav Pandey, Tushar Garg
Collaborators: Chris Ding, Xiaofeng He, Ya Zhang, Stephen R. Holbrook
Research supported by NSF, IBM
Protein Function and Interaction Data
• Proteins usually interact with other proteins to perform their
function(s)
• Interaction data provides a glimpse into the mechanisms
underlying biological processes
– Networks of pairwise protein-protein interactions
– Protein complexes
• Neighboring proteins in an interaction network tend to
perform similar functions
– Several computational approaches proposed for predicting
protein function from interaction networks [Pandey et al, 2006]
• A group of proteins occurring in many complexes may
represent a functional modules that consists of proteins
involved in similar biological processes
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Problems with Available Interaction
Data (I)
• Noise: Spurious or false positive interactions
Hart et
al,2006
• Leads to significant fall in performance of protein
function prediction algorithms [Deng et al, 2003]
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Problems with Available Interaction
Data (II)
• Incompleteness: Unavailability of a major fraction
of interactomes of major organisms
Hart et al, 2006
• Yeast: 50%, Human: 11%
• May delay the discovery of important knowledge
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Overview
This talk is about using association
analysis to address these limitations of
protein interaction data
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Noise removal from protein interaction data
• Two types of patterns
– Itemsets: Collection of items
• Example: {Milk, Diaper}
– Association Rules: X  Y, where X
and Y are itemsets.
• Example: Milk  Diaper
Nov 26, 2007
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 
# transacti ons that contain X and Y
# transacti ons that contain X
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
C
D
E
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)
Confidence is used because
of its interpretation as
conditional probability
Nov 26, 2007
B
AB
ABCD

null
Has well-known limitations
ABCE
ABDE
ACDE
BCDE
ABCDE
Given d items, there are 2d
possible candidate itemsets
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
There are lots of
measures proposed
in the literature
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
The H-confidence Measure
 The h-confidence of a pattern P = {i1, i2,…, im}
 Illustration:
 A pattern P is a hyperclique pattern if hconf(P)>=hc, where
hc is a user specified minimum h-confidence threshold
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
 Magnitude of relationship consistent with many other
measures
 Jaccard, Correlation, Cosine
 Cross support property
• Eliminates patterns involving items that have very different support levels
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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)
Nov 26, 2007

supp{X}
supp{Y}
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Applications of
H-confidence/Hypercliques
• Pattern-preserving clustering [Xiong et al, 2004, SDM]
• Reducing privacy leakage in databases [Xiong et al,
2006c, VLDB Journal]
• Noise removal [Xiong et al, 2006b, IEEE TKDE]
– Data points not a member of any hypercliques hypothesized to
be noisy
– Improved performance of several data analysis tasks
(association analysis, clustering) on several types of data sets
(text, microarray data)
– Illustrates noise resistance property of hypercliques and hconfidence
• Discovery of functional modules from protein complexes
[Xiong et al, 2005, PSB]
• Noise-resistant transformation of protein interaction
networks [Pandey et al, 2007, KDD]
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
I. Application of Association Analysis:
Identification of Protein Function Modules
Complexes
Proteins
c1
p1, p2
c2
p1, p3, p4, p5
c3
p2, p3, p4, p6
 Published in Xiong et al [2005], PSB
 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)
 Hyperclique derived from this data can be
used to discover frequently occurring
groups of proteins in several complexes
Likely to constitute functional modules
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
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 Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
)
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
More Hyperclique Examples..
# distinct proteins in cluster = 12
# proteins in one group = 12
# distinct proteins in cluster = 8
# proteins in one group = 8
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
More Hyperclique Examples..
# distinct proteins in cluster = 12
# proteins in one group = 12
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
More Hyperclique Examples..
# distinct proteins in cluster = 10
# proteins in one group = 9
(rest denoted as
Nov 26, 2007
)
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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)
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Clustering Results – GO
Hierarchies
# distinct proteins in cluster = 5
# proteins in one group = 5
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
# distinct proteins
in cluster = 6
# proteins in one
group = 6
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Clustering Results – GO
Hierarchies
# distinct proteins in cluster = 7
# proteins in one group = 4
(Rest of the 3 proteins are marked
as )
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
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
Nov 26, 2007
)
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Summary of Results
 Hypercliques show great promise for identifying
protein modules and for annotating
uncharacterized proteins
 Clustering does not perform as well as
hypercliques 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 be of different sizes
• Data is high-dimensional
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Application II: Association Analysis-based
Pre-processing of Protein Interaction Networks
• Overall Objective: Accurate inference of protein function
from interaction networks
• Complexity: Noise and incompleteness in interaction
networks adversely impact accuracy of functional
inferences [Deng et al, 2003]
• Potential Approach: Pre-processing of interaction
networks
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Our Approach
• Transform graph G=(V,E,W) into G’=(V,E’,W’)
Transformed PPI
graph where Pi
and Pj are
connected if
(Pi,Pj) is a
hyperclique
pattern
Input PPI
graph
• Tries to meet three objectives:
– Addition of potentially biologically valid edges
– Removal of potentially noisy edges
– Assignment of weights to the resultant set of edges that indicate
their reliability
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Pair-wise H-Confidence
• Measure of the affinity of two items in terms of the
transactions in which they appear simultaneously [Xiong et
al, 2006]
• For an interaction network represented as an adjacency
matrix:
– Unweighted Networks: n1,n2=# neighbors of p1,p2
m=# shared neighbors of p1,p2
– Weighted Networks: n1,n2=sum(weights) of edges incident on p1,p2
m = sum of min(weights) of edges to common
neighbors of p1,p2
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Related Approaches: Neighborhoodbased Similarity
i
j
i
j
• Motivation: Two proteins sharing several common neighbors are
likely to have a valid interaction
• Probability (p-value) of having m common neighbors given degrees
of the two proteins n1 and n2, and size of the network N [Samanta et
al, 2003]
• Handles the problem of high degree nodes
• # common neighbors or Jacquard similarity (m/(n1+n2-m)) [Brun et
al, 2003]
• Min(fractions of common neighbors) = Min(m/n1, m/n2)
– Identical to pairwise h-confidence
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
H-confidence Example
Unweighted Network
p1
p2
p3
p4
p5
p1
0
0
1
1
1
p2
0
0
1
1
0
p3
1
1
0
0
1
p4
0
1
0
0
1
p5
1
0
1
1
0
Hconf(p1,p2)= min(0.5,0.5)
= 0.5
Nov 26, 2007
Weighted Network
p1
p2
p3
p4
p5
p1 p2 p 3 p4 p5
0
0 0.5 0 0.1
0
0
1 0.2 0
0.5 1
0
0 0.1
0 0.2 0
0 0.5
0.1 0 0.1 0.5 0
Hconf(p1,p2)= min(0.5/0.6,0.5/1.2)
= 0.416
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Sparsification to remove spurious edges
Common neighborbased transformation
# edges = 6490
Nov 26, 2007
Pruning to remove
spurious edges
# edges = 95739
# edges = 6874
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Validation of Final Network
• Use FunctionalFlow algorithm [Nabieva et al, 2005] on
the original and transformed graph(s)
– One of the most accurate algorithms for predicting function from
interaction networks
– Produces likelihood scores for each protein being annotated with
one of 75 MIPS functional labels
• Likelihood matrix evaluated using two metrics
– Multi-label versions of precision and recall:
mi = # predictions made, ni = # known annotations, ki = # correct predictions
– Precision/accuracy of top-k predictions
• Useful for actual biological experimental scenarios
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Test Protein Interaction Networks
• Three yeast interaction networks with different types of
weighting schemes used for experiments
– Combined
• Composed from Ito, Uetz and Gavin (2002)’s data sets
• Individual reliabilities obtained from EPR index tool of DIP
• Overall reliabilities obtained using a noisy-OR
– [Krogan et al, 2006]’s data set
• 6180 interactions between 2291 annotated proteins
• Edge reliabilities derived using machine learning techniques
– DIPCore [Deane et al, 2002]
• ~5K highly reliable interactions in DIP
• No weights assigned: assumed unweighted
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Results on Combined data set
Precision-Recall
Nov 26, 2007
Accuracy of top-k
predictions
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Results on Krogan et al’s data set
Precision-Recall
Nov 26, 2007
Accuracy of top-k
predictions
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Results on DIPCore
Precision-Recall
Nov 26, 2007
Accuracy of top-k
predictions
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Noise removal capabilities of Hconfidence
• H-confidence and
hypercliques have been
shown to have noise removal
capabilities [Xiong et al,
2006]
• To test its effectiveness, we
added 50% random edges to
DIPCore, and re-ran the
transformation process
• Fall in performance of
transformed network is
significantly smaller than
that in the original network
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
Summary of Results
• H-confidence-based transformations generally produce
more accurate and more reliably weighted interaction
graphs: Validated function prediction
• Generally, the less reliable the weights assigned to the
edges in the raw network, the greater improvement in
performance obtained by using an h-confidence-based
graph transformation.
• Better performance of the h-confidence-based graph
transformation method is indeed due to the removal of
spurious edges, and potentially the addition of
biologically viable ones and effective weighting of the
resultant set of edges.
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
References (I)
[Pandey et al, 2006] Gaurav Pandey, Vipin Kumar and Michael Steinbach, Computational Approaches for Protein
Function Prediction: A Survey, TR 06-028, Department of Computer Science and Engineering, University of
Minnesota, Twin Cities
[Pandey et al, 2007] G. Pandey, M. Steinbach, R. Gupta, T. Garg and V. Kumar, Association analysis-based
transformations for protein interaction networks: a function prediction case study. KDD 2007: 540-549
[Xiong et al, 2005] XIONG, H., HE, X., DING, C., ZHANG, Y., KUMAR, V., AND HOLBROOK, S. R. 2005. Identification
of functional modules in protein complexes via hyperclique pattern discovery. In Proc. Pacific Symposium on
Biocomputing (PSB). 221–232.
[Xiong et al, 2006a] XIONG, H., TAN, P.-N., AND KUMAR, V. 2003. Hyperclique Pattern Discovery, Data Mining and
Knowledge Discovery, 13(2):219-242
[Xiong et al, 2006b] XIONG, H., PANDEY, G., STEINBACH, M., AND KUMAR, V. 2006, Enhancing Data Analysis with
Noise Removal, IEEE TKDE, 18(3):304-319
[Xiong et al, 2006c] Hui Xiong, Michael Steinbach, and Vipin Kumar, Privacy Leakage in Multi-relational Databases: A
Semi-supervised Learning Perspective, VLDB Journal Special Issue on Privacy Preserving Data Management ,
Vol. 15, No. 4, pp. 388-402, November, 2006
[Xiong et al, 2004] Hui Xiong, Michael Steinbach, Pang-Ning Tan and Vipin Kumar, HICAP: Hierarchical Clustering with
Pattern Preservation, SIAM Data Mining 2004
[Tan et al, 2005] TAN, P.-N., STEINBACH, M., AND KUMAR, V. 2005. Introduction to Data Mining. Addison-Wesley.
[Nabieva et al, 2005] NABIEVA, E., JIM, K., AGARWAL, A., CHAZELLE, B., AND SINGH, M. 2005. Whole-proteome
prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21, Suppl. 1, i1–i9.
[Deng et al, 2003] DENG, M., SUN, F., AND CHEN, T. 2003. Assessment of the reliability of protein–protein interactions
and protein function prediction. In Pac Symp Biocomputing. 140–151.
[Gavin et al, 2002] A. Gavin et al. Functional organization of the yeast proteome by systematic analysis of protein
complexes, Nature, 415:141-147, 2002
[Hart et al, 2006] G Traver Hart, Arun K Ramani and Edward M Marcotte, How complete are current yeast and human
protein-interaction networks, Genome Biology, 7:120, 2006
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›
References (II)
[Brun et al, 2003] BRUN, C., CHEVENET, F.,MARTIN, D.,WOJCIK, J., GUENOCHE, A., AND JACQ, B. 2003.
Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network.
Genome Biology 5, 1, R6
[Samanta et al, 2003] SAMANTA, M. P. AND LIANG, S. 2003. Predicting protein functions from redundancies in largescale protein interaction networks. Proc Natl Acad Sci U.S.A. 100, 22, 12579–12583
[Salwinski et al, 2004] Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The Database of
Interacting Proteins: 2004 update. NAR 32 Database issue:D449-51, http://dip.doe-mbi.ucla.edu/
[Gavin et al, 2006] Gavin et al, 2006, Proteome survey reveals modularity of the yeast cell machinery, Nature 440, 631636
[Deane et al, 2002] Deane CM, Salwinski L, Xenarios I, Eisenberg D (2002) Protein interactions: Two methods for
assessment of the reliability of high-throughput observations. Mol Cell Prot 1:349-356
Nov 26, 2007
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data
‹#›