NetBioSIG2013-KEYNOTE Natasa Przulj
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Transcript of NetBioSIG2013-KEYNOTE Natasa Przulj
July 19, 2013
Nataša Pržulj
Network Topology as a Source of Biological
Information
Imperial College LondonDepartment of Computing
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Networks → biological information
2
Networks can model: gene interactions protein structure protein-protein interactions metabolism …
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model: gene interactions protein structure protein-protein interactions metabolism …
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model: gene interactions protein structure protein-protein interactions metabolism …
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model: gene interactions protein structure protein-protein interactions metabolism …
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model: gene interactions protein structure protein-protein interactions metabolism …
Turning point in biology and bioinformatics
Advances in experimental biology data Interesting & important problems to CS Computational advances contribute:
Biological understanding (disease, pathogens, aging)
Therapeutics healthcare benefits (e.g., GSK)
Booming research area
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:• Network topology – new source of biological information• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:● Revolutionized our understanding of:
Biology Diseases Evolution
Networks:● Similar ground-breaking impact
3Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:• Network topology – new source of biological information• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:● Revolutionized our understanding of:
Biology Diseases Evolution
Networks:● Similar ground-breaking impact
3Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:• Network topology – new source of biological information• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:● Revolutionized our understanding of:
Biology Diseases Evolution
Networks:● Similar ground-breaking impact
3Networks → biological information
100% sequence identity 65% network wiring similarity
Degrees 54 and 9
V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:• Network topology – new source of biological information• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:● Revolutionized our understanding of:
Biology Diseases Evolution
Networks:● Similar ground-breaking impact
3Networks → biological information
100% sequence identity 65% network wiring similarity
Degrees 54 and 9
V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:• Network topology – new source of biological information• Based on results (ERC, NSF): topology ↔ biology
Need tools to mine networks
Why?● Analysing sequences is “computationally easy” (polynomial
time)● Analysing networks (i.e., graphs) is “computationally hard”
E.g., Is X sub-network of Y? ̶ Computationally intractable Cannot exactly compare / align biological networks
heuristics (approximate solutions)
3Networks → biological information
100% sequence identity 65% network wiring similarity
Degrees 54 and 9
V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
4
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
4
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.
Networks → biological information
Graphlet Degree Vector (GDV) of node u:
GDV(u) = (u0, u1, u2, …, u72)
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
4
Why? Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?→ hub proteins: lethal?→ …
Frustration: network analyses useless?
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.
Networks → biological information
Graphlet Degree Vector (GDV) of node u:
GDV(u) = (u0, u1, u2, …, u72)
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → Results:
5
Why? Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?→ hub proteins: lethal?→ …
Frustration: network analyses useless?
90% similar topology ↔ significantly enriched:
→ Biological function→ Protein complexes→ Sub-cellular localization→ Tissue expression→ Disease
1. T. Milenković & N. Pržulj, Cancer Informatics, 4:257-273, 2008. (Highly visible)
Networks → biological information
SMD1
SMB1RPO26
5
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → Results:Why? Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?→ hub proteins: lethal?→ …
Frustration: network analyses useless?
Cancer research:
→ Find new members of melanin production pathways: phenotypically validated (siRNA)
→ Same cancer type - more similar topology in PPI net
→ Could not have been identified by existing approaches
2. T. Milenković, V. Memisević, A. K. Ganesan, and N. Pržulj, J. Roy. Soc. Interface, 7(44):423-437, 2010. 3. H. Ho, T. Milenković, V. Memisević, J. Aruri, N. Pržulj, and A. K. Ganesan, BMC Systems Biology, 4:84, 2010. (Highly accessed)
Networks → biological information
55
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → Results:Why? Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?→ hub proteins: lethal?→ …
Frustration: network analyses useless?
Find new members of yeast proteosome PPI network
4. C. Guerrero, T. Milenkovic, N. Przulj, P. Keiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis,” PNAS, 105 (36), pg. 13333-13338 2008.)
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
Network alignment – approximate subnetwork finding
6
Networks → biological information
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → GRAAL family:
Network alignment – approximate subnetwork finding Why?
Analogous to sequence alignment Predict function, disease − by knowledge transfer Evolution − global similarity between networks of different species
Problems: Noise in the data → all methods must be robust to noise Computational intractability → computational problems:
Node similarity function? Alignment search algorithm? How to measure “goodness” of an inexact fit between networks?
…
6
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-
1354, 2010T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly
visible)
Networks → biological information
GRAAL: 267 nodes and 900 edges
Isorank: 116 nodes and 261 edges
MI-GRAAL: 1,858 nodes and 3,467 edges
6
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → GRAAL family:
Network alignment – approximate subnetwork finding
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-
1354, 2010T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly
visible)
Networks → biological information
6
GRAAL: 267 nodes and 900 edges
Isorank: 116 nodes and 261 edges
MI-GRAAL: 1,858 nodes and 3,467 edges
6
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → GRAAL family:
Network alignment – approximate subnetwork finding
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-
1354, 2010T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly
visible)
Networks → biological information
R. Patro and C. Kingsford. Global network alignment using multiscale spectral signatures. Bioinformatics 28(23):3105-3114 (2012).
Dr. Noel Malod-Dognin, GrAlign + Poster - L103
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
7Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
4. New graphlet-based measures: disease associations and network dynamics
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors Analysis of it in the context of the entire human PPI network Analysis of this new network Predictions of new GPCRs
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors Analysis of it in the context of the entire human PPI network Analysis of this new network Predictions of new GPCRs
“spine” of the network functionally separates the cell topologically separates the
cell
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors Analysis of it in the context of the entire human PPI network Analysis of this new network Predictions of new GPCRs
“core” of the network 25 disease genes: mostly brain disorders
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
Vuk, Anida: Poster - O065Poster - O046
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors Analysis of it in the context of the entire human PPI network Analysis of this new network Predictions of new GPCRs
11 proteins “similar” to 6 GPCRs Predicted new GPCRs: e.g., chromosome 20 open
reading frame 39 (TMEM90B)
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
Vuk, Anida: Poster - O065Poster - O046
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
9Networks → biological information
Some current development:
2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
New method for integration / fusion of molecular network data
Currently primitive “projection” methods Purely descriptive Provide no conceptual framework for predictions
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
New method for integration / fusion of molecular network dataBased on: matrix representation of the data their fusion by:
simultaneous matrix factorization and mining of the resulting decomposition
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-targetGene annotation
Gene-disease
Metabolic netDO
GODrug inter.
4 Objects: Genes, GO terms, DO terms, Drugs
Constraints: Ѳi
Relation matrices: Rij
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
New method for integration / fusion of molecular network dataPPIs
Co-expression
Cell signalling
Genetic inter.
Drug-targetGene annotation
Gene-disease
Metabolic netDO
GODrug inter.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Alg. 1: Data fusion by matrix factorization:PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-targetGene annotation
Gene-disease
Metabolic netDO
GODrug inter.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Alg. 2: Disease class and association prediction:PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-targetGene annotation
Gene-disease
Metabolic netDO
GODrug inter.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Some Results:PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-targetGene annotation
Gene-disease
Metabolic netDO
GODrug inter.
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Some Results: DO ∩ disease class − DO (pathological analysis and clinical symptoms)
from only molecular data
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-targetGene annotation
Gene-disease
Metabolic netDO
GODrug inter.
X
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12Networks → biological information
Some current development:
4. New graphlet-based measures: network dynamics, disease classification,...
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12Networks → biological information
Network 1 Network 2
Distance = 1.675
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12Networks → biological information
Some current development:
4. New graphlet-based measures: network dynamics, disease classification,...
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12Networks → biological information
Some current development:
4. New graphlet-based measures: network dynamics, disease classification
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
1) New network analysis methods to mine complex network data Network alignment Cell’s functional organization Network integration/fusion of various network types Graphlet-based network encoding for dynamics ...
2) High-performance software package
13
Summary
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Software
Easy to use for biologists Open source Parallel
Benefit biologists: Methods ready to use
Allow benchmarking To come: web interface
13
GraphCrunch 2: second most accessed in BMC3400 downloads since Feb’11
Software
O. Kuchaiev, A. Stefanovic, W. Hayes, and N. Przulj, GraphCrunch 2: Software tool for network modeling, alignment and clustering, BMC Bioinformatics, 12(24):1-13, 2011 (highly accessed)T. Milenkovic, J. Lai, and N. Przulj, GraphCrunch: A Tool for Large Network Analyses, BMC Bioinformatics, 9:70, January 30, 2008 (highly accessed)
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Final Remarks
Network topology – contains currently hidden biological information
Need new computational tools to mine network data biology
In close collaboration with biologists “Network biology:”
• In its infancy & rich in open research problems• Many unforeseen problems will emerge• Good area to be in
14
Acknowledgements
Funding: ERC Starting Grant, €1.6M (2012-2017)NSF CDI: $2M (2010 — 2014)NSF CAREER: $570K (Jan. 2007 — Dec.
2011) GlaxoSmithKline: £80K (2010-2014)
Alumni:1. Tijana Milenković, Ph.D.
Assistant Prof., U. of Notre Dame
2. Oleksii Kuchaiev, Ph.D.Microsoft, Redmond
3. Vesna Memišević, Ph.D.US Army, Bioinformatics Res.
15
Acknowledgements
Funding: ERC Starting Grant, €1.6M (2012-2017)NSF CDI: $2M (2010 — 2014)NSF CAREER: $570K (Jan. 2007 — Dec.
2011) GlaxoSmithKline: £80K (2010-2014)
Post-docs:Noel Malod-Dognin
PhD students: Omer Yaveroglu, Kai Sun, Vuk Janjic, Anida Sarajlic
15
1. W. Hayes, K. Sun, and N. Przulj, Graphlet-based measures are suitable for biological network comparison, Bioinformatics, 2013
2. V. Janic and N. Przulj, The Core Diseasome, Molecular BioSystems, 8:2614-2625, July 4, 20123. V. Janic and N. Przulj, Biological function through network topology: a survey of the human diseasome, Briefings in
Functional Genomics, September 8, 20124. Arabidopsis Interactome Mapping Consortium, Evidence for Network Evolution in an Arabidopsis Interactome Map,
Science, 333:601-607, July 29, 20115. T. Milenkovic, V. Memisevic and N. Przulj, Dominating Biological Networks, PLoS ONE, 6(8):e23016, 20116. N. Pržulj, “Protein-protein interactions: making sense of networks via graph-theoretic modeling,” Bioessays, 33(2), 2011.7. O. Kuchaiev and N. Przulj, “Integrative Network Alignment Reveals Large Regions of Global Network Similarity in Yeast and Human”,
Bioinformatics, 27(10): 1390-1396 , 2011.8. O. Kuchaiev, A. Stevanovic, W. Hayes and N. Przulj, “GraphCrunch 2: software tool for network modeling, alignment and clustering”,
BMC Bioinformatics, 12(24):1-13, 2011. Highly accessed.9. T. Milenkovic, W. L. Ng, W. Hayes and N. Przulj, “Optimal Network Alignment Using Graphlet Degree Vectors”, Cancer Informatics,
9:121-137, 2010. Highly visible.10. O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes and N. Przulj, “Topological Network Alignment Uncovers Biological Function and
Phylogeny”, J. Roy Soc. Interface, 7:1341–1354, 2010.11. N. Przulj, O. Kuchaiev, A. Stevanovic, and W. Hayes “Geometric Evolutionary Dynamics of Protein Interaction Network”, Pacific
Symposium on Biocomputing (PSB’10), Hawaii, USA, 2010.12. T. Milenkovic, V. Memisevic, A. K. Ganesan, and N. Przulj, “Systems-level Cancer Gene Identification from Protein Interaction Network
Topology Applied to Melanogenesis-related Interaction Networks”, J. Roy. Soc. Interface, 2009.13. O. Kuchaiev, M. Rasajski, D. Higham, and N. Przulj, “Geometric De-noising of Protein-Protein Interaction Networks”, PLoS
Computational Biology 5(8), e1000454, 2009.14. C. Guerrero, T. Milenkovic, N. Przulj, P. Keiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-
based tag-team strategy and protein interaction network analysis,” PNAS, 105 (36), pg. 13333-13338 2008.15. T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, vol. 4, pg.
257-273, 2008. Highly visible.16. T. Milenkovic, J. Lai, N. Przulj, “GraphCrunch: A Tool for Large Network Analyses,” BMC Bioinform., 9:70, 2008. Highly accessed.17. F. Hormozdiari, P. Berenbrink, N. Przulj, C. Sahinalp, “Not all Scale Free Networks are Born Equal: the Role of the Seed Graph in PPI
Network Emulation,” PLoS Computational Biology, 3(7), 2007.18. N. Przulj, “Geometric Local Structure in Biological Networks,” IEEE ITW’07 Invited Paper, 2007.19. N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” Bioinformatics proc. of ECCB’06,23:e177-e183, 2007.20. N. Przulj and D. Higham, “Modelling Protein-Protein Interaction Networks via a Stickiness Index,” J Roy Soc Interf, 3(10):711-6,2006. 21. N. Przulj, D. G. Corneil, and I. Jurisica, “Efficient Estimation of Graphlet Frequency Distributions in Protein-Protein Interaction
Networks,” Bioinformatics, vol. 22, num. 8, pg 974-980, 2006.22. M. Barrios-Rodiles, K. R. Brown, B. Ozdamar, Z. Liu, R. S. Donovan, F. Shinjo, Y. Liu, R. Bose, J. Dembowy, I. W. Taylor, V. Luga, N.
Przulj, M. Robinson, H. Suzuki, Y. Hayashizaki, I. Jurisica, and J. L. Wrana, “High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells,” Science, vol. 307, num. 5715, pg. 1621-1625, 2005.
23. N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale-Free or Geometric?,” Bioinformatics, 20(18):3508-3515, 2004.24. N. Przulj, D. Wigle, and I. Jurisica, “Functional Topology in a Network of Protein Interactions,” Bioinformatics, 20(3):340-348, 2004.