Automatic Parameter Learning for Multiple Network Alignment

Flannick, Jason; Novak, Antal F.; Chuong, B.; Srinivasan, Balaji; Batzoglou, Serafim

doi:10.1007/978-3-540-78839-3_19

Cited by 79 publications

(73 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 We do not compare our alignment to the one produced by Graemlin because Graemlin requires a variety of other input information, including phylogenetic relationships between the species being aligned. In contrast, GRAAL's output can be used to infer phylogenetic relationships.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…19,27,28 Unlike the above algorithms that primarily aim to detect conserved subnetworks, IsoRank 27 aims to maximize the overall match between the two networks. It relies on spectral graph theory to compute scores of aligning pairs of nodes from different networks; it does so by using the heuristic that two nodes are a good match if their respective neighbors also match well.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Topological Network Alignment Uncovers Biological Function and Phylogeny

Kuchaiev

Milenković

Memišević

et al. 2009

SciVee

View full text Add to dashboard Cite

Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein-protein interaction networks of two very different species-yeast and human-indicate that even distant species share a surprising amount of network topology with each other, suggesting broad similarities in internal cellular wiring across all life on Earth.

show abstract

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Topological Network Alignment Uncovers Biological Function and Phylogeny

Kuchaiev

Milenković

Memišević

et al. 2009

SciVee

View full text Add to dashboard Cite

show abstract

“…These include NetworkBLAST [Sharan et al 2005], MAWISH [Koyutürk et al 2006] NetAlign [Liang et al 2006], Graemlin [Flannick et al 2006[Flannick et al , 2008, IsoRank [Singh et al 2007[Singh et al , 2008, GRAAL [Kuchaiev et al 2010], Natalie [Klau 2009], Natalie 2.0 [El-Kebir et al 2011] and the algorithm of Bradde et al [2010]. Some of these tools have extensions for aligning more than two networks, but we focus on the two network case here.…”

Section: Finding Common Pathways In Biological Networkmentioning

confidence: 99%

“…This problem is motivated by applications in several areas including biology, computer vision, and natural language processing. For example, the study of protein interactions across different species has made network alignment a common topic in computational biology [Flannick et al 2006[Flannick et al , 2008Klau 2009;Kuchaiev et al 2009;Singh et al 2007Singh et al , 2008. In computer vision, network alignment is used for matching images [Conte et al 2004;Schellewald and Schnörr 2005], and in the ontology alignment, it is used for finding correspondence between different representations of a database [Lacoste-Julien et al 2006;Melnik et al 2002;Šváb 2007].…”

Section: Introductionmentioning

confidence: 99%

Message-Passing Algorithms for Sparse Network Alignment

Bayati

Gleich

Saberi

et al. 2013

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

Network alignment generalizes and unifies several approaches for forming a matching or alignment between the vertices of two graphs. We study a mathematical programming framework for network alignment problem and a sparse variation of it where only a small number of matches between the vertices of the two graphs are possible. We propose a new message passing algorithm that allows us to compute, very efficiently, approximate solutions to the sparse network alignment problems with graph sizes as large as hundreds of thousands of vertices. We also provide extensive simulations comparing our algorithms with two of the best solvers for network alignment problems on two synthetic matching problems, two bioinformatics problems, and three large ontology alignment problems including a multilingual problem with a known labeled alignment.

show abstract

“…Analogous to sequence-based comparisons, network comparisons across species have also been used to identify proteins with similar functions and detect orthologs [1,6,7,8,9,10,11,12]. However, almost all of these network comparison methods rely mostly on sequence information and use only limited network topological information.…”

mentioning

confidence: 99%

Complementarity of network and sequence information in homologous proteins

Memišević

Milenković

Pržulj

2010

Journal of Integrative Bioinformatics

View full text Add to dashboard Cite

SummaryTraditional approaches for homology detection rely on finding sufficient similarities between protein sequences. Motivated by studies demonstrating that from non-sequence based sources of biological information, such as the secondary or tertiary molecular structure, we can extract certain types of biological knowledge when sequence-based approaches fail, we hypothesize that protein-protein interaction (PPI) network topology and protein sequence might give insights into different slices of biological information. Since proteins aggregate to perform a function instead of acting in isolation, analyzing complex wirings around a protein in a PPI network could give deeper insights into the protein's role in the inner working of the cell than analyzing sequences of individual genes. Hence, we believe that one could lose much information by focusing on sequence information alone. We examine whether the information about homologous proteins captured by PPI network topology differs and to what extent from the information captured by their sequences. We measure how similar the topology around homologous proteins in a PPI network is and show that such proteins have statistically significantly higher network similarity than nonhomologous proteins. We compare these network similarity trends of homologous proteins with the trends in their sequence identity and find that network similarities uncover almost as much homology as sequence identities. Although none of the two methods, network topology and sequence identity, seems to capture homology information in its entirety, we demonstrate that the two might give insights into somewhat different types of biological information, as the overlap of the homology information that they uncover is relatively low. Therefore, we conclude that similarities of proteins' topological neighborhoods in a PPI network could be used as a complementary method to sequence-based approaches for identifying homologs, as well as for analyzing evolutionary distance and functional divergence of homologous proteins.

show abstract

Automatic Parameter Learning for Multiple Network Alignment

Cited by 79 publications

References 29 publications

Topological Network Alignment Uncovers Biological Function and Phylogeny

Topological Network Alignment Uncovers Biological Function and Phylogeny

Message-Passing Algorithms for Sparse Network Alignment

Complementarity of network and sequence information in homologous proteins

Contact Info

Product

Resources

About