vipin kumar university of minnesota [email protected] cs.umn/~kumar
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Association Analysis-based Extraction of Functional Information from
Protein-Protein Interaction Data
Vipin KumarUniversity 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 2
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 3
Problems with Available Interaction Data (I)
• Noise: Spurious or false positive interactions
• Leads to significant fall in performance of protein function prediction algorithms [Deng et al, 2003]
Hart et al,2006
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 4
Problems with Available Interaction Data (II)
• Incompleteness: Unavailability of a major fraction of interactomes of major organisms
• Yeast: 50%, Human: 11%• May delay the discovery of important knowledge
Hart et al, 2006
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 5
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 6
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
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
Set-Based Representation of Data
ons transactiTotal
Y and Xcontain that ons transacti# s Support,
Xcontain that ons transacti#
Y and Xcontain that ons transacti# c ,Confidence
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 7
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
- Support has anti-monotone property: X Y implies (Y) ≤ (X)
Confidence is used because of its interpretation as conditional probability
Has well-known limitations
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
Association Analysis
Given d items, there are 2d possible candidate itemsets
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 8
There are lots of measures proposed in the literature
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 9
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 10
Alternate Equivalent Definitions of h-confidence
Given a pattern P = {i1, i2,…, im}
• Definition:
• Definition:
1 2( ) min{ ({ } { { }}) | { , ,..., }}mhconf P conf x P x x i i i
1 2( ) min{ ( ) | , { , ,..., }& }mhconf P conf X Y X Y i i i X Y P
All-Confidence Measure
Omiecinski – TKDE 2003
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 11
Properties of Hyperclique Pattern
Anti-monotone
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
' , ( ') ( )if P P then hconf P hconf P
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 12
Cross Support Property of h-confidence
Support distribution of the pumsb dataset
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
hconf(P)
&X Y P X Y
supp{X} supp{Y}
,X Y P
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 13
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 h-confidence
• 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 14
I. Application of Association Analysis: Identification of Protein Function Modules
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
Complexes Proteins
c1 p1, p2
c2 p1, p3, p4, p5
c3 p2, p3, p4, p6
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 15
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 16
Hyperclique Patterns from Protein Complex Data
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
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
5 Rpn6 Rpt2 Rpn12 Rpn3 Rpn8
5 Ada2 Gcn5 Rpo21 Spt7 Taf60
6 YLR033W Ioc3 Npl6 Rsc2 Itc1 Rpc40
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 Prp40 Prp42 Prp6 Smd1 Snt309 Snu56 Srb2 YDL209C Clf1 Lea1 Luc7 Prp4 Rse1 Smb1 Smd3 Snp1 Snu66 Snu71 YLR424W
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)
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 17
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 18
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 19
More Hyperclique Examples
# distinct proteins in cluster = 13
# proteins in one group = 10
(rest denoted as )
# distinct proteins in cluster = 13
# proteins in one group = 12
(rest denoted as )
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 20
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 21
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 22
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 23
More Hyperclique Examples.. 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
# distinct proteins in cluster = 39
# proteins in one group = 32
# proteins at node ‘mRNA splicing’ = 37
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 24
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 25
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 h-confidence)
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 26
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 27
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 28
Clustering Results – GO Hierarchies
# distinct proteins in cluster = 6
# proteins in one group = 6
# 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 29
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 30
Clustering Results – GO Hierarchies
# distinct proteins in cluster = 11
# proteins in one group = 10
Protein SKN1 (denoted by ) involved in metabolism got clustered together with proteins involved in cellular physiological process
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 31
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 32
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 33
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 34
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 35
Our Approach
• Transform graph G=(V,E,W) into G’=(V,E’,W’)
• 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
Input PPI graph
Transformed PPI graph where Pi
and Pj are connected if
(Pi,Pj) is a hyperclique
pattern
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 36
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 37
Related Approaches: Neighborhood-based Similarity
• 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
i j i j
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 38
H-confidence Example
p1 p2 p3 p4 p5
p1 0 0 1 0 1
p2 0 0 1 1 0
p3 1 1 0 0 1
p4 1 1 0 0 1
p5 1 0 1 1 0
p1 p2 p3 p4 p5
p1 0 0 0.5 0 0.1
p2 0 0 1 0.2 0
p3 0.5 1 0 0 0.1
p4 0 0.2 0 0 0.5
p5 0.1 0 0.1 0.5 0
Unweighted Network Weighted Network
Hconf(p1,p2)= min(0.5,0.5) = 0.5
Hconf(p1,p2)= min(0.5/0.6,0.5/1.2) = 0.416
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 39
Sparsification to remove spurious edges
Common neighbor-based transformation
Pruning to removespurious edges
# edges = 6490 # edges = 95739 # edges = 6874
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 40
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 41
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 42
Results on Combined data set
Precision-Recall Accuracy of top-k predictions
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 43
Results on Krogan et al’s data set
Precision-Recall Accuracy of top-k predictions
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 44
Results on DIPCore
Precision-Recall Accuracy of top-k predictions
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 45
Noise removal capabilities of H-confidence
• 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 46
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 47
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 48
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 large-scale 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, 631-636
[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