Tijana Milenković
Assistant Professor
Computer Science & Engineering
University of Notre Dame
Novel directions for biologi...
ISMB posters (O – systems biology and networks):
• O-05
• O-08
• O-09
• O-22
tmilenko@nd.edu
Complex Networks (CoNe) Group
www.nd.edu/~cone/
Joseph
Crawford
Yuriy
Hulovatyy
Fazle
Faisal
Vikram
Saraph
tmilenko@nd.edu
Networks are everywhere!
Complex Networks (CoNe) Group
• Develop new algorithms for network “mining”
• Use the algorithms to study real-world netwo...
Network alignment
Across-species transfer of biological knowledge
tmilenko@nd.edu
• Map “similar” nodes between different networks
in a way that conserves edges
Network alignment
tmilenko@nd.edu
• IsoRank family (B. Berger, MIT, 2007-2009)
• Our methods (2010):
– GRAAL
O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Ha...
Mix-and-match existing methods to improve them
• Network alignment – algorithmic components:
1. Node cost function (NCF)
2...
Mix-and-match existing methods to improve them
• Network alignment – algorithmic components:
1. Node cost function (NCF)
2...
Mix-and-match existing methods to improve them
• Network alignment – algorithmic components:
1. Node cost function (NCF)
2...
Mix-and-match existing methods to improve them
• Our goal: mix and match node cost functions and
alignment strategies of s...
MAGNA: Maximizing Accuracy
in Global Network Alignment
• Existing methods:
– Rapidly identify from all possible alignments...
• MAGNA:
– Directly optimizes edge conservation while the
alignment is constructed
– Can optimize any alignment quality me...
• Key idea behind MAGNA:
– Cross parent alignments into a superior child alignment
• Parent alignments:
– Alignments of ex...
• MAGNA on synthetic networks
MAGNA: Maximizing Accuracy
in Global Network Alignment
tmilenko@nd.edu
MAGNA: Maximizing Accuracy
in Global Network Alignment
tmilenko@nd.edu
• MAGNA on real-world (biological) networks
MAGNA: Maximizing Accuracy
in Global Network Alignment
tmilenko@nd.edu
• Running time comparison
– MAGNA is run on random ...
Network alignment in aging
Current knowledge about human aging
• Human aging - hard to study experimentally
– Long lifespa...
• But, genes, i.e., their protein products, carry out
biological processes by interacting with each other
• And this is ex...
Network alignment in aging
So, predict novel “ground truth” knowledge
about human aging via network alignment
tmilenko@nd....
• GenAge: ~250 genes (3!)
• We predict novel aging-related candidates:
– 792 genes in human
– 311, 522, and 544 genes in y...
Other projects in my group
• E.g., dynamic network analysis
F.E. Faisal and T. Milenković, “Dynamic networks reveal key pl...
Other projects in my group
• E.g., network clustering
R.W. Solava, R.P. Michaels, and T. Milenkovic, “Graphlet-based edge ...
Other projects in my group
• E.g., network de-noising via link prediction
Y. Hulovatyy, R.W. Solava, and T. Milenkovic, “R...
Protein synthesis and folding
(with Patricia Clark)
Protein degradation (with Lan Huang)
R. Kaake, T. Milenkovic, N. Przulj, P. Kaiser, and L. Huang, Journal of Proteome Rese...
Netsense (with Aaron Striegel)
How do individuals interact in the “always-on” environment?
L. Meng, T. Milenković, and A. ...
Physiological networks (with Sidney D’Mello)
Y. Hulovatyy, S. D’Mello, R. Calvo, T. Milenković, “Network Analysis Improves...
Acknowledgements
• NSF CCF-1319469 ($453K)
• NSF EAGER CCF-1243295 ($208K)
• NIH R01 Supplement 3R01GM074807-07S1 ($249K)
...
25. B. Yoo, H. Chen, F.E. Faisal, T. Milenković, "Improving identification of key players in aging via network de-noising"...
NetBioSIG2014-Talk by Tijana Milenkovic
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NetBioSIG2014-Talk by Tijana Milenkovic

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NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014

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  • Analogous to genomic sequence research, biological network research is expected to impact our biological understanding, since genes, that is their protein products, carry out most biological processes by interacting with other proteins, and this is exactly what biological networks model. Thus, computational prediction of protein function and the role of proteins in disease from PPI networks have received attention in the post-genomic era.
  • NetBioSIG2014-Talk by Tijana Milenkovic

    1. 1. Tijana Milenković Assistant Professor Computer Science & Engineering University of Notre Dame Novel directions for biological network alignment - MAGNA
    2. 2. ISMB posters (O – systems biology and networks): • O-05 • O-08 • O-09 • O-22 tmilenko@nd.edu
    3. 3. Complex Networks (CoNe) Group www.nd.edu/~cone/ Joseph Crawford Yuriy Hulovatyy Fazle Faisal Vikram Saraph tmilenko@nd.edu
    4. 4. Networks are everywhere!
    5. 5. Complex Networks (CoNe) Group • Develop new algorithms for network “mining” • Use the algorithms to study real-world networks – Focus on biological (molecular) networks tmilenko@nd.edu
    6. 6. Network alignment Across-species transfer of biological knowledge tmilenko@nd.edu
    7. 7. • Map “similar” nodes between different networks in a way that conserves edges Network alignment tmilenko@nd.edu
    8. 8. • IsoRank family (B. Berger, MIT, 2007-2009) • Our methods (2010): – GRAAL O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, N. Przulj, "Topological network alignment uncovers biological function and phylogeny", Journal of the Royal Society Interface, 2010. – H-GRAAL T. Milenkovic, W.L. Ng, W. Hayes, N. Przulj, “Optimal Network Alignment with Graphlet Degree Vectors”, Cancer Informatics, 2010. • MI-GRAAL (N. Przulj, ICL, 2011) • GHOST (C. Kingsford, CMU, 2012) • … • Mix-and-match existing methods to improve them – F.E. Faisal, H. Zhao, and T. Milenković, “Global Network Alignment In The Context Of Aging”, IEEE/ACM TCBB, 2014. Also, in ACM-BCB 2013. • MAGNA – V. Saraph and T. Milenković, “MAGNA: Maximizing Accuracy of Global Network Alignment”, Bioinformatics, 2014. Network alignment tmilenko@nd.edu
    9. 9. Mix-and-match existing methods to improve them • Network alignment – algorithmic components: 1. Node cost function (NCF) 2. Alignment strategy (AS) tmilenko@nd.edu
    10. 10. Mix-and-match existing methods to improve them • Network alignment – algorithmic components: 1. Node cost function (NCF) 2. Alignment strategy (AS) tmilenko@nd.edu
    11. 11. Mix-and-match existing methods to improve them • Network alignment – algorithmic components: 1. Node cost function (NCF) 2. Alignment strategy (AS) tmilenko@nd.edu
    12. 12. Mix-and-match existing methods to improve them • Our goal: mix and match node cost functions and alignment strategies of state-of-the-art methods – MI-GRAAL and IsoRankN • Fair evaluation framework • New superior method? YES! • Follow-up study on MI-GRAAL and GHOST – Same conclusions J. Crawford, Y. Sun, and T. Milenković, “Fair evaluation of global network aligners”, submitted, 2014. tmilenko@nd.edu
    13. 13. MAGNA: Maximizing Accuracy in Global Network Alignment • Existing methods: – Rapidly identify from all possible alignments the “high- scoring” alignments with respect to total NCF – Evaluate alignments with respect to edge conservation – So, align similar nodes between networks hoping to conserve many edges (after the alignment is constructed!) tmilenko@nd.edu
    14. 14. • MAGNA: – Directly optimizes edge conservation while the alignment is constructed – Can optimize any alignment quality measure • E.g., a measure of both node and edge conservation – Outperforms existing state-of-the-art methods • In terms both node and edge conservation • In terms of both topological and biological quality MAGNA: Maximizing Accuracy in Global Network Alignment tmilenko@nd.edu
    15. 15. • Key idea behind MAGNA: – Cross parent alignments into a superior child alignment • Parent alignments: – Alignments of existing methods – Or completely random alignments – Evolve as long as allowed by computational resources Software: http://nd.edu/~cone/MAGNA MAGNA: Maximizing Accuracy in Global Network Alignment tmilenko@nd.edu
    16. 16. • MAGNA on synthetic networks MAGNA: Maximizing Accuracy in Global Network Alignment tmilenko@nd.edu
    17. 17. MAGNA: Maximizing Accuracy in Global Network Alignment tmilenko@nd.edu • MAGNA on real-world (biological) networks
    18. 18. MAGNA: Maximizing Accuracy in Global Network Alignment tmilenko@nd.edu • Running time comparison – MAGNA is run on random alignments
    19. 19. Network alignment in aging Current knowledge about human aging • Human aging - hard to study experimentally – Long lifespan – Ethical constraints • Hence, sequence-based knowledge transfer from model species • I.e., current “ground truth” - computational predictions • But – Not all genes in model species have human orthologs (vice versa) – Importantly, genes’ “connectivities” typically ignored tmilenko@nd.edu
    20. 20. • But, genes, i.e., their protein products, carry out biological processes by interacting with each other • And this is exactly what biological networks model! – E.g., protein-protein interaction (PPI) networks Network alignment in aging tmilenko@nd.edu
    21. 21. Network alignment in aging So, predict novel “ground truth” knowledge about human aging via network alignment tmilenko@nd.edu
    22. 22. • GenAge: ~250 genes (3!) • We predict novel aging-related candidates: – 792 genes in human – 311, 522, and 544 genes in yeast, fruitfly, and worm • Examples of validation – Significant overlap with independent “ground truth” data – Significantly enriched diseases: • Brain tumor • Prostate cancer • Cancer – Literature validation: 91% of our top scoring predictions Network alignment in aging tmilenko@nd.edu
    23. 23. Other projects in my group • E.g., dynamic network analysis F.E. Faisal and T. Milenković, “Dynamic networks reveal key players in aging”, Bioinformatics, 2014.
    24. 24. Other projects in my group • E.g., network clustering R.W. Solava, R.P. Michaels, and T. Milenkovic, “Graphlet-based edge clustering reveals pathogen- interacting proteins”, Bioinformatics, ECCB 2012 (acceptance rate: 14%).
    25. 25. Other projects in my group • E.g., network de-noising via link prediction Y. Hulovatyy, R.W. Solava, and T. Milenkovic, “Revealing missing parts of the interactome via link prediction”, PLOS ONE, 2014. B. Yoo, H. Chen, F.E. Faisal, and T. Milenkovic, “Improving identification of key players in aging via network de-noising”, ACM-BCB 2014.
    26. 26. Protein synthesis and folding (with Patricia Clark)
    27. 27. Protein degradation (with Lan Huang) R. Kaake, T. Milenkovic, N. Przulj, P. Kaiser, and L. Huang, Journal of Proteome Research, 2010. C. Guerrero, T. Milenkovic, N. Przulj, J. J. Jones, P. Kaiser, L. Huang, PNAS, 2008.
    28. 28. Netsense (with Aaron Striegel) How do individuals interact in the “always-on” environment? L. Meng, T. Milenković, and A. Striegel, “Systematic Dynamic and Heterogeneous Analysis of Rich Social Network Data,” Complex Networks V, 2014. L. Meng, Y. Hulovatyy, A. Striegel, and T. Milenković, “On the Interplay Between Individuals' Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks”, submitted, 2014.
    29. 29. Physiological networks (with Sidney D’Mello) Y. Hulovatyy, S. D’Mello, R. Calvo, T. Milenković, “Network Analysis Improves Interpretation of Affective Physiological Data,” Journal of Complex Networks, 2014. Also, in IEEE Proceedings of Complex Networks, 2013.
    30. 30. Acknowledgements • NSF CCF-1319469 ($453K) • NSF EAGER CCF-1243295 ($208K) • NIH R01 Supplement 3R01GM074807-07S1 ($249K) • Google Faculty Research Award ($33K) tmilenko@nd.edu
    31. 31. 25. B. Yoo, H. Chen, F.E. Faisal, T. Milenković, "Improving identification of key players in aging via network de-noising", ACM-BCB 2014. 24. L. Meng, Y. Hulovatyy, A. Striegel, T. Milenković, "On the Interplay Between Individuals' Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks", submitted, 2014. 23. V. Saraph, T. Milenković, "MAGNA: Maximizing Accuracy in Global Network Alignment", Bioinformatics, DOI: 10.1093/bioinformatics/btu409, 2014. 22. Y. Hulovatyy, S. D'Mello, R.A. Calvo, T. Milenković, "Network Analysis Improves Interpretation of Affective Physiological Data", Journal of Complex Networks, DOI: 10.1093/comnet/cnu032, 2014. 21. F.E. Faisal, H. Zhao, T. Milenković, "Global Network Alignment In The Context Of Aging", IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2014.2326862, 2014. 20. F.E. Faisal, T. Milenković, "Dynamic networks reveal key players in aging", Bioinformatics, DOI: 10.1093/bioinformatics/btu089, 2014. 19. L. Meng, T. Milenković, A. Striegel, "Systematic Dynamic and Heterogeneous Analysis of Rich Social Network Data", In Proceedings of Complex Networks V, 2014 (acceptance rate: 25%). 18. A.K. Rider, T. Milenković, G.H. Siwo, R.S. Pinapati, S.J. Emrich, M.T. Ferdig, N.V. Chawla, "Networks’ Characteristics Matter for Systems Biology," Network Science, accepted, to appear, 2014. 17. Y. Hulovatyy, R.W. Solava, T. Milenković, “Revealing missing parts of the interactome via link prediction”, PLOS ONE, 9(3), 2014. 16. Y. Hulovatyy, S. D'Mello, R.A. Calvo, T. Milenković, “Network Analysis Improves Interpretation of Affective Physiological Data”, In Proceedings of Workshop on Complex Networks and their Applications at SITIS 2013, DOI: 10.1109/SITIS.2013.82. 15. T. Milenković, H. Zhao, and F.E. Faisal (2013), “Global Network Alignment In The Context Of Aging”, In Proceedings of ACM-BCB 2013 (acceptance rate: 28%). 14. R. Solava, R. Michaels, T. Milenković, “Graphlet-based edge clustering reveals pathogen-interacting genes,” In Proceedings of ECCB 2012, Bioinformatics, 28 (18): i480-i486, 2012. 13. T. Milenković, V. Memišević, A. Bonato, N. Pržulj, “Dominating biological networks,” PLOS ONE, 6(8), 2011. 12. Arabidopsis Interactome Mapping Consortium, "Evidence for Network Evolution in an Arabidopsis Interactome Map," Science, 333(6042):601-607, 2011. 11. T. Milenković, W.L. Ng, W. Hayes, N. Pržulj, “Optimal network alignment with graphlet degree vectors,” Cancer Informatics, 9, 2010. 10. R. Kaake, T. Milenković, N. Pržulj, P. Kaiser, L. Huang, “Characterization of cell cycle specific protein interaction networks of the yeast 26S proteasome complex by the QTAX strategy,” Journal of Proteome Research, 9(4): 2016-2029, 2010. 9. H. Ho, T. Milenković, V. Memišević, J. Aruri, N. Pržulj, A.K. Ganesan, “Protein Interaction Network Topology Uncovers Melanogenesis Regulatory Network Components Within Functional Genomics Datasets,” BMC Systems Biology, 4:84, 2010 (Highly Accessed). 8. V. Memišević, T. Milenković, N. Pržulj,“Complementarity of network and sequence structure in homologous proteins,” Journal of Integrative Bioinformatics, 7(3):135, 2010. 7. Memišević, T. Milenković, N. Pržulj, “An integrative approach to modeling biological networks,” Journal of Integrative Bioinformatics, 7(3):135, 2010. 6. O. Kuchaiev, T. Milenković, V. Memišević, W. Hayes, N. Pržulj, “Topological network alignment uncovers biological function and phylogeny,” Journal of the Royal Society Interface, 7:1341-1354, 2010. 5. T. Milenković, V. Memišević, A.K. Ganesan, N. Pržulj, “Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data,” Journal of the Royal Society Interface, 7(44), 423-437, 2010. 4. T. Milenković, I. Filippis, M. Lappe, N. Pržulj, “Optimized Null Model of Protein Structure Networks,” PLOS ONE, 4(6): e5967, 2009. 3. C. Guerrero, T. Milenković , N. Pržulj, P. Kaiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis,” PNAS, 105(36), 13333-13338, 2008. 2. T. Milenković & N. Pržulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, 2008:6 257-273, 2008 (Highly Visible). 1. T. Milenković, J. Lai,N. Pržulj, “GraphCrunch: A Tool for Large Network Analyses,” BMC Bioinformatics, 9:70, 2008 (Highly Accessed).

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